Connection to DB¶

This is the connection link to my database on postgreSQL, the actual connection function is on the file db_connect.py

# Import necessary packages
import pandas as pd
from db_connect import connect_to_db

# Step 1: Connect to the database
conn = connect_to_db()

# Step 2: Create a cursor and run a query
cursor = conn.cursor()
query = "SELECT * FROM food_prices_cleaned.food_prices_kenya;"
cursor.execute(query)

# Step 3: Fetch results and convert to a DataFrame
rows = cursor.fetchall()
df = pd.DataFrame(rows, columns=[desc[0] for desc in cursor.description])

# Step 4: Display the data
print("Connection successful! Previewing data:")
display(df.head(25))

Database connection successful!
Connection successful! Previewing data:

Data Exploration with Python, tryna get to understand my data

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 41400 entries, 0 to 41399
Data columns (total 34 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   provinces                   41400 non-null  object 
 1   counties                    41400 non-null  object 
 2   mkt_name                    41400 non-null  object 
 3   latitude                    41400 non-null  float64
 4   longitude                   41400 non-null  float64
 5   geo_id                      41400 non-null  object 
 6   price_date                  41400 non-null  object 
 7   year                        41400 non-null  int64  
 8   month                       41400 non-null  int64  
 9   components                  41400 non-null  object 
 10  o_beans                     41400 non-null  float64
 11  h_beans                     41400 non-null  float64
 12  l_beans                     41400 non-null  float64
 13  c_beans                     41400 non-null  float64
 14  inflation_beans             39192 non-null  float64
 15  trust_beans                 41400 non-null  float64
 16  o_maize                     41400 non-null  float64
 17  h_maize                     41400 non-null  float64
 18  l_maize                     41400 non-null  float64
 19  c_maize                     41400 non-null  float64
 20  inflation_maize             39192 non-null  float64
 21  trust_maize                 41400 non-null  float64
 22  o_potatoes                  41400 non-null  float64
 23  h_potatoes                  41400 non-null  float64
 24  l_potatoes                  41400 non-null  float64
 25  c_potatoes                  41400 non-null  float64
 26  inflation_potatoes          39192 non-null  float64
 27  trust_potatoes              41400 non-null  float64
 28  o_food_price_index          41400 non-null  float64
 29  h_food_price_index          41400 non-null  float64
 30  l_food_price_index          41400 non-null  float64
 31  c_food_price_index          41400 non-null  float64
 32  inflation_food_price_index  39192 non-null  float64
 33  trust_food_price_index      41400 non-null  float64
dtypes: float64(26), int64(2), object(6)
memory usage: 10.7+ MB

df.describe()

Data Manipulation and Modification¶

Standardising potatoes data to price per KG¶

Divide potatoes price columns by 50 to standardize to price per 1kg I choose to do this on derived columns to avoid confusion, or incase i'll need the original data in future.

df['o_potatoes_1kg'] = df['o_potatoes'] / 50
df['h_potatoes_1kg'] = df['h_potatoes'] / 50
df['l_potatoes_1kg'] = df['l_potatoes'] / 50
df['c_potatoes_1kg'] = df['c_potatoes'] / 50

confirm if the additional potatoes columns have been added¶

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 41400 entries, 0 to 41399
Data columns (total 38 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   provinces                   41400 non-null  object 
 1   counties                    41400 non-null  object 
 2   mkt_name                    41400 non-null  object 
 3   latitude                    41400 non-null  float64
 4   longitude                   41400 non-null  float64
 5   geo_id                      41400 non-null  object 
 6   price_date                  41400 non-null  object 
 7   year                        41400 non-null  int64  
 8   month                       41400 non-null  int64  
 9   components                  41400 non-null  object 
 10  o_beans                     41400 non-null  float64
 11  h_beans                     41400 non-null  float64
 12  l_beans                     41400 non-null  float64
 13  c_beans                     41400 non-null  float64
 14  inflation_beans             39192 non-null  float64
 15  trust_beans                 41400 non-null  float64
 16  o_maize                     41400 non-null  float64
 17  h_maize                     41400 non-null  float64
 18  l_maize                     41400 non-null  float64
 19  c_maize                     41400 non-null  float64
 20  inflation_maize             39192 non-null  float64
 21  trust_maize                 41400 non-null  float64
 22  o_potatoes                  41400 non-null  float64
 23  h_potatoes                  41400 non-null  float64
 24  l_potatoes                  41400 non-null  float64
 25  c_potatoes                  41400 non-null  float64
 26  inflation_potatoes          39192 non-null  float64
 27  trust_potatoes              41400 non-null  float64
 28  o_food_price_index          41400 non-null  float64
 29  h_food_price_index          41400 non-null  float64
 30  l_food_price_index          41400 non-null  float64
 31  c_food_price_index          41400 non-null  float64
 32  inflation_food_price_index  39192 non-null  float64
 33  trust_food_price_index      41400 non-null  float64
 34  o_potatoes_1kg              41400 non-null  float64
 35  h_potatoes_1kg              41400 non-null  float64
 36  l_potatoes_1kg              41400 non-null  float64
 37  c_potatoes_1kg              41400 non-null  float64
dtypes: float64(30), int64(2), object(6)
memory usage: 12.0+ MB

Renaming some columns for better understanding of what they represent

import pandas as pd

def rename_agric_columns(df):
    """
    Renames columns like o_beans, h_beans, c_maize, etc. 
    to a consistent format such as beans_open, maize_high, etc.
    """
    rename_map = {}
    prefix_map = {
        'o': 'open',
        'h': 'high',
        'l': 'low',
        'c': 'close'
    }

    # Iterate through existing columns
    for col in df.columns:
        # Check for trading-style prefixes (o_, h_, l_, c_)
        for prefix, new_prefix in prefix_map.items():
            if col.startswith(f"{prefix}_"):
                # Example: o_beans → beans_open
                rename_map[col] = f"{col.split('_', 1)[1]}_{new_prefix}"
                break
        # Handle inflation_* and trust_* as is
        if col.startswith("inflation_") or col.startswith("trust_"):
            rename_map[col] = col  # keep same (optional)
    
    # Apply renaming
    df = df.rename(columns=rename_map)
    return df

df = rename_agric_columns(df)

df.columns

Index(['provinces', 'counties', 'mkt_name', 'latitude', 'longitude', 'geo_id',
       'price_date', 'year', 'month', 'components', 'beans_open', 'beans_high',
       'beans_low', 'beans_close', 'inflation_beans', 'trust_beans',
       'maize_open', 'maize_high', 'maize_low', 'maize_close',
       'inflation_maize', 'trust_maize', 'potatoes_open', 'potatoes_high',
       'potatoes_low', 'potatoes_close', 'inflation_potatoes',
       'trust_potatoes', 'food_price_index_open', 'food_price_index_high',
       'food_price_index_low', 'food_price_index_close',
       'inflation_food_price_index', 'trust_food_price_index',
       'potatoes_1kg_open', 'potatoes_1kg_high', 'potatoes_1kg_low',
       'potatoes_1kg_close'],
      dtype='object')

In this step i'm grouping the data by province, then ordering it by year: This keeps all rows but arranges them so that: All rows from the same province are grouped together Within each province, data appears in chronological order

df = df.sort_values(['provinces', 'year']).reset_index(drop=True)
df

VISUALS ¶

Visualizing trends by Time(Yearly and Monthly)¶

General Trends and Overview¶

What are the overall trends in food prices (beans, maize, potatoes, and the food price index) across Kenya over the years (2007–2025)?

We want to see how prices have changed over time, for each commodity across all regions. That means we’ll probably focus on averages per year (national trend), not per province yet.

We'll start by calculating the mean closing price for each commodity per year:

Yearly Trends for Food¶

yearly_trends = df.groupby('year')[['beans_close','maize_close','potatoes_1kg_close','food_price_index_close']].mean().reset_index()
yearly_trends

Overall Food Price Trends in Kenya by Year (2007–2025):¶

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(12, 6))
sns.lineplot(data=yearly_trends, x='year', y='beans_close', label='Beans')
sns.lineplot(data=yearly_trends, x='year', y='maize_close', label='Maize')
sns.lineplot(data=yearly_trends, x='year', y='potatoes_1kg_close', label='Potatoes')
sns.lineplot(data=yearly_trends, x='year', y='food_price_index_close', label='Food_Price_Index')

plt.title('Overall Food Price Trends in Kenya (2007–2025)', fontsize=14)
plt.xlabel('Year')
plt.ylabel('Average Closing Price')
plt.legend(title='Commodity')
plt.grid(True, linestyle='--', alpha=0.5)
plt.tight_layout()
plt.show()

/home/kobey/anaconda3/envs/medium_article/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"

Breakdown based on the Line chart above¶

1. Upward Trend Overall:¶

All commodities beans, maize, and potatoes show a gradual increase in average prices from 2007 through around 2023, followed by a slight decline toward 2025.
This suggests long-term inflationary pressure on food prices in Kenya.

2. Beans are consistently the most expensive:¶

The blue line(Beans) remains well above maize and potatoes throughout the period. This likely reflects both higher production costs and strong demand for beans as a protein source

3. Parallel movement between maize and potatoes¶

The Orange(maize) and green(Potatoes) line move roughly together, meaning price changes for one often coincide with the other This may reflect shared market influence like weather condition or fuel prices that affect all stable crops.

4. Food Price Index follows the same direction:¶

Even though its values are smaller in scale, the Food Price Index (red line) mirrors the general direction of the other commodities. It acts as a summary indicator of overall food inflation, showing peaks and troughs that align with the crops’ price changes.

5. Notable peaks (2022–2023):¶

There’s a sharp spike across all commodities around 2022–2023, likely due to global and local disruptions e.g., drought, COVID-19 aftereffects, global supply chain issues or elections. After this spike, prices dip slightly toward 2025, suggesting a partial recovery or stabilization.

6. Notable Dips¶

There are two visible dips, around 2010 and 2018, across most commodities.
These years coincide with major election periods in Kenya (the 2010 constitutional referendum and the 2017 general election).
Such events often influence food prices through market disruptions, political uncertainty, and short-term policy changes that affect production and distribution.

Monthly Trends for Food¶

monthly_trends = df.groupby(['year','month'])[['beans_close','maize_close','potatoes_1kg_close','food_price_index_close']].mean().reset_index()
monthly_trends

Visuals For Monthly Trends¶

import calendar

def month_to_name(x):
    # If it's an integer (1–12), convert to month name
    if isinstance(x, int):
        return calendar.month_name[x]
    # If it's already a string (e.g. "January"), keep it as is
    return x

monthly_trends['month'] = monthly_trends['month'].apply(month_to_name)

Heat map for Beans¶

# Make sure month names are properly formatted and ordered
month_order = list(calendar.month_name[1:])

# Convert month column to an ordered categorical type
monthly_trends['month'] = pd.Categorical(monthly_trends['month'],
                                         categories=month_order,
                                         ordered=True)

# pivot will respect that order
pivot = monthly_trends.pivot(index='year', columns='month', values='beans_close')

plt.figure(figsize=(10,6))
sns.heatmap(pivot, cmap='RdYlGn_r', annot=True, fmt=".1f")
plt.title('Seasonal Heatmap of Beans (Monthly Averages)')
plt.xlabel('Month')
plt.ylabel('Year')
plt.show()

Heat map for Maize¶

# Make sure month names are properly formatted and ordered
month_order = list(calendar.month_name[1:])

# Convert month column to an ordered categorical type
monthly_trends['month'] = pd.Categorical(monthly_trends['month'],
                                         categories=month_order,
                                         ordered=True)

# pivot will respect that order
pivot = monthly_trends.pivot(index='year', columns='month', values='maize_close')

plt.figure(figsize=(10,6))
sns.heatmap(pivot, cmap='RdYlGn_r', annot=True, fmt=".1f")
plt.title('Seasonal Heatmap of Maize (Monthly Averages)')
plt.xlabel('Month')
plt.ylabel('Year')
plt.show()

Heat map for potatoes¶

# Make sure month names are properly formatted and ordered
month_order = list(calendar.month_name[1:])

# Convert month column to an ordered categorical type
monthly_trends['month'] = pd.Categorical(monthly_trends['month'],
                                         categories=month_order,
                                         ordered=True)

# pivot will respect that order
pivot = monthly_trends.pivot(index='year', columns='month', values='potatoes_1kg_close')

plt.figure(figsize=(10,6))
sns.heatmap(pivot, cmap='RdYlGn_r', annot=True, fmt=".1f")
plt.title('Seasonal Heatmap of Potatoes (Monthly Averages)')
plt.xlabel('Month')
plt.ylabel('Year')
plt.show()

Heat map for Food Prices Index¶

import calendar
# Make sure month names are properly formatted and ordered
month_order = list(calendar.month_name[1:])

# Convert month column to an ordered categorical type
monthly_trends['month'] = pd.Categorical(monthly_trends['month'],
                                         categories=month_order,
                                         ordered=True)

# pivot will respect that order
pivot = monthly_trends.pivot(index='year', columns='month', values='food_price_index_close')

plt.figure(figsize=(10,6))
sns.heatmap(pivot, cmap='RdYlGn_r', annot=True, fmt=".1f")
plt.title('Seasonal Heatmap of Food Price Index (Monthly Averages)')
plt.xlabel('Month')
plt.ylabel('Year')
plt.show()

Interpretation of the Heatmap: Seasonal Food Price Index (2007–2025)¶

1.General Trend Over Time (2007-2025)¶

The color intensity deepens gradually from top to bottom, indicating that food prices have generally increased over the years. Earlier years (2007–2010) show light yellow-green colors (index ≈ 0.4–0.8), while recent years (2023–2025) show dark blue shades (index ≈ 1.3–1.7). Conclusion: There’s a clear long-term upward trend in food prices, reflecting inflation, cost of production, or other macroeconomic factors.

2.Seasonal (Month-to-Month) Variation¶

Prices tend to peak between May and August (darker shades). January to April often shows lighter colors, relatively lower food prices. September to December stabilizes or slightly cools off, depending on the year. Interpretation: This suggests seasonal price pressure mid-year, possibly linked to: Planting or lean seasons before harvest (reduced supply). Increased demand or transport challenges in those months. Conversely, lower prices early in the year may correspond to harvest periods when supply is abundant.

3. Exceptional Years¶

2023 shows the darkest overall colors (1.6–1.8 range), indicating exceptionally high prices likely due to inflationary pressures or external shocks (e.g., global food shortages, climate effects). 2010 and 2016 show relatively cooler colors even mid-year, meaning prices were comparatively stable during those periods. Conclusion: Prices have become consistently high but less seasonally volatile in recent years.

Visualizing trends by Regions(Province, County and Market)¶

Regions that consistently have the highest or lowest food prices By Province¶

Average Closing Price of Food Per Province¶

# Step 1: Group by provinces and year to get average closing prices per year
avg_prices_province = (
    df.groupby(['provinces','year'], as_index=False)[['beans_close', 'maize_close','potatoes_1kg_close','food_price_index_close']].mean()
)
avg_prices_province

Visualize the table above (Average Closing Price of Food Per Province)¶

# Step 1: Reshape your data from wide to long format
price_columns = ['beans_close', 'maize_close', 'potatoes_1kg_close', 'food_price_index_close']
df_long = avg_prices_province.melt(
    id_vars=['provinces', 'year'],
    value_vars=price_columns,
    var_name='Commodity',
    value_name='Price'
)

print("--- Head of Reshaped (Long) Data ---")
print(df_long.head())
print("\n")

# 2. Plot the data using sns.relplot
sns.set_style("whitegrid")

# This creates a Figure-level plot that automatically handles faceting
g = sns.relplot(
    data=df_long,
    kind='line',          # Specify a line plot
    x='year',
    y='Price',
    hue='Commodity',      # Color lines by commodity
    col='provinces',      # Create columns for each province
    col_wrap=3,           # Wrap the columns after 3 plots
    height=3.5,           # Height of each individual plot
    aspect=1.5,           # Aspect ratio (width/height)
    legend='full'
)

# Step 3: Customize titles and labels
g.set_axis_labels('Year', 'Average Closing Price')
g.set_titles(col_template='{col_name}')
g.fig.suptitle('Food Price Trends by Province (2007–2025)', fontsize=16, y=1.03) # Add a main title

# 4. Save the figure
#plt.tight_layout()
plt.savefig('province_price_trends.png')

print("Faceted plot saved to 'province_price_trends.png'")

--- Head of Reshaped (Long) Data ---
  provinces  year    Commodity      Price
0   Central  2007  beans_close  43.306667
1   Central  2008  beans_close  74.847500
2   Central  2009  beans_close  77.480833
3   Central  2010  beans_close  60.275833
4   Central  2011  beans_close  90.697500


Faceted plot saved to 'province_price_trends.png'

Province with consistently the Highest and Lowest Food Price¶

# Created a New Column average_food_price
avg_prices_province['avg_food_price'] = avg_prices_province[
   ['beans_close', 'maize_close', 'potatoes_1kg_close']
].mean(axis=1)

Highest & Lowest Overall Average Food Prices By Province¶

# Compute the long-term average for each province
province_avg_overall = (
    avg_prices_province.groupby('provinces', as_index=False)['avg_food_price']
    .mean()
    .sort_values(by='avg_food_price', ascending=False)
)

# Display results
print(province_avg_overall)

# Get the top province
top_province = province_avg_overall.iloc[0]
bottom_province = province_avg_overall.iloc[-1]
print(f"Province with consistently highest food price: {top_province['provinces']} ({top_province['avg_food_price']:.2f})")
print(f"Province with consistently lowest food price: {bottom_province['provinces']} ({bottom_province['avg_food_price']:.2f})")

       provinces  avg_food_price
4  North Eastern       61.944615
3        Nairobi       59.616837
6    Rift Valley       58.805979
2        Eastern       56.963456
0        Central       56.529800
1          Coast       54.771665
5         Nyanza       53.851025
Province with consistently highest food price: North Eastern (61.94)
Province with consistently lowest food price: Nyanza (53.85)

Higest & Lowest Overall Prices by Province for each crop¶

# List of commodities to analyze
crops = ['beans_close', 'maize_close', 'potatoes_1kg_close', 'food_price_index_close']

for crop in crops:
    crop_name = crop.replace('_close', '').capitalize()
    print(f"\n🌾 Ranking of provinces by average {crop_name} price:\n")

    # Compute average price per province
    province_avg = (
        avg_prices_province.groupby('provinces', as_index=False)[crop]
        .mean()
        .sort_values(by=crop, ascending=False)
    )

    # Print full ranking
    for i, row in province_avg.iterrows():
        print(f"{i+1}. {row['provinces']}: {row[crop]:.2f}")

    # Identify top and bottom provinces
    top = province_avg.iloc[0]
    bottom = province_avg.iloc[-1]

    # Print summary
    print(f"\n🏆 Highest average {crop_name} price: {top['provinces']} ({top[crop]:.2f})")
    print(f"💰 Lowest average {crop_name} price: {bottom['provinces']} ({bottom[crop]:.2f})")
    print("-" * 60)

🌾 Ranking of provinces by average Beans price:

5. North Eastern: 102.25
4. Nairobi: 101.45
7. Rift Valley: 101.34
3. Eastern: 93.33
2. Coast: 92.49
1. Central: 91.42
6. Nyanza: 88.98

🏆 Highest average Beans price: North Eastern (102.25)
💰 Lowest average Beans price: Nyanza (88.98)
------------------------------------------------------------

🌾 Ranking of provinces by average Maize price:

5. North Eastern: 39.39
1. Central: 36.14
3. Eastern: 35.71
4. Nairobi: 35.65
7. Rift Valley: 34.29
6. Nyanza: 33.01
2. Coast: 32.68

🏆 Highest average Maize price: North Eastern (39.39)
💰 Lowest average Maize price: Coast (32.68)
------------------------------------------------------------

🌾 Ranking of provinces by average Potatoes_1kg price:

5. North Eastern: 44.19
1. Central: 42.03
3. Eastern: 41.85
4. Nairobi: 41.74
7. Rift Valley: 40.78
6. Nyanza: 39.56
2. Coast: 39.15

🏆 Highest average Potatoes_1kg price: North Eastern (44.19)
💰 Lowest average Potatoes_1kg price: Coast (39.15)
------------------------------------------------------------

🌾 Ranking of provinces by average Food_price_index price:

5. North Eastern: 1.05
4. Nairobi: 1.01
7. Rift Valley: 1.00
3. Eastern: 0.97
1. Central: 0.96
2. Coast: 0.93
6. Nyanza: 0.92

🏆 Highest average Food_price_index price: North Eastern (1.05)
💰 Lowest average Food_price_index price: Nyanza (0.92)
------------------------------------------------------------

Regions that consistently have the highest or lowest food prices By County¶

Average Closing price of Food per County¶

# Step 2: Group by counties and year to get average closing prices per year
avg_prices_county = (
    df.groupby(['counties','year'], as_index=False)[['beans_close', 'maize_close','potatoes_1kg_close','food_price_index_close']].mean()
)
avg_prices_county

Visualize the table above (Average Closing Price of Food Per County)¶

# Step 1: Keep province info for later merge
county_province_map = df[['counties', 'provinces']].drop_duplicates()

# Step 2: Group by counties and year (correct averaging)
avg_prices_county = (
    df.groupby(['counties', 'year'], as_index=False)[
        ['beans_close', 'maize_close', 'potatoes_1kg_close', 'food_price_index_close']
    ].mean()
)

# Step 3: Merge province info back
avg_prices_county = avg_prices_county.merge(county_province_map, on='counties', how='left')

# Step 4: Append province name to county
avg_prices_county['county_label'] = avg_prices_county['counties'] + " (" + avg_prices_county['provinces'] + ")"

# Step 5: Reshape for visualization
price_columns = ['beans_close', 'maize_close', 'potatoes_1kg_close', 'food_price_index_close']
df_long = avg_prices_county.melt(
    id_vars=['provinces', 'county_label', 'year'],
    value_vars=price_columns,
    var_name='Commodity',
    value_name='Price'
)

# Step 6: Plot
sns.set_style("whitegrid")

g = sns.relplot(
    data=df_long,
    kind='line',
    x='year',
    y='Price',
    hue='Commodity',
    col='county_label',    # use new county label
    col_wrap=3,
    height=3.5,
    aspect=1.5,
    legend='full'
)

g.set_axis_labels('Year', 'Average Closing Price')
g.set_titles(col_template='{col_name}')
g.fig.suptitle('Food Price Trends by County (2007–2025)', fontsize=16, y=1.03)

plt.savefig('county_price_trends_labeled.png', bbox_inches='tight')
print("Faceted plot saved to 'county_price_trends_labeled.png'")

Faceted plot saved to 'county_price_trends_labeled.png'

Counties with consistently the Highest & Lowest Food Price¶

# Created a New Column average_food_price
avg_prices_county['avg_food_price'] = avg_prices_county[
    ['beans_close', 'maize_close', 'potatoes_1kg_close']
].mean(axis=1)
avg_prices_county

Highest & Lowest Overall Average Food Prices By county¶

# Compute the long-term average for each County
county_avg_overall = (
    avg_prices_county.groupby('counties', as_index=False)['avg_food_price']
    .mean()
    .sort_values(by='avg_food_price', ascending=False)
)

# Display results
print(county_avg_overall)

# Get the top & bottom County
top_county = county_avg_overall.iloc[0]
bottom_county = county_avg_overall.iloc[-1]
print(f"County with consistently highest food price: {top_county['counties']} ({top_county['avg_food_price']:.2f})")
print(f"County with consistently lowest food price: {bottom_county['counties']} ({bottom_county['avg_food_price']:.2f})")

        counties  avg_food_price
23         Wajir       63.705852
21       Turkana       63.455772
10       Mandera       62.450173
1        Garissa       60.877888
15       Nairobi       59.616837
11      Marsabit       59.035170
2         Isiolo       57.636550
17         Nyeri       56.529800
8       Machakos       56.230134
20    Tana River       56.217690
3        Kajiado       55.728368
4         Kilifi       54.770632
12    Meru North       54.318829
16        Nakuru       54.172254
5         Kisumu       53.851025
6          Kitui       53.719964
13    Meru South       53.655970
14       Mombasa       53.358758
24    West Pokot       52.861940
9        Makueni       52.855744
7          Kwale       52.599195
18       Samburu       52.502900
19  Taita Taveta       52.309043
0        Baringo       49.603472
22   Uasin Gishu       49.594992
County with consistently highest food price: Wajir (63.71)
County with consistently lowest food price: Uasin Gishu (49.59)

Higest & Lowest Overall Prices by county for each crop¶

# List of commodities to analyze
crops = ['beans_close', 'maize_close', 'potatoes_1kg_close', 'food_price_index_close']

for crop in crops:
    crop_name = crop.replace('_close', '').capitalize()
    print(f"\n🌾 Ranking of counties by average {crop_name} price:\n")

    # Compute average price per county
    county_avg = (
        avg_prices_county.groupby('counties', as_index=False)[crop]
        .mean()
        .sort_values(by=crop, ascending=False)
    )

    # Print full ranking
    for i, row in county_avg.iterrows():
        print(f"{i+1}. {row['counties']}: {row[crop]:.2f}")

    # Identify top and bottom county
    top = county_avg.iloc[0]
    bottom = county_avg.iloc[-1]

    # Print summary
    print(f"\n🏆 Highest average {crop_name} price: {top['counties']} ({top[crop]:.2f})")
    print(f"💰 Lowest average {crop_name} price: {bottom['counties']} ({bottom[crop]:.2f})")
    print("-" * 60)

🌾 Ranking of counties by average Beans price:

22. Turkana: 110.47
24. Wajir: 106.61
11. Mandera: 103.39
16. Nairobi: 101.45
12. Marsabit: 101.17
2. Garissa: 99.67
5. Kilifi: 98.50
15. Mombasa: 94.78
3. Isiolo: 94.48
25. West Pokot: 94.18
8. Kwale: 92.56
21. Tana River: 91.42
18. Nyeri: 91.42
9. Machakos: 90.67
17. Nakuru: 89.50
4. Kajiado: 89.33
6. Kisumu: 88.98
19. Samburu: 88.05
14. Meru South: 86.35
13. Meru North: 86.28
1. Baringo: 85.64
23. Uasin Gishu: 84.31
10. Makueni: 80.81
20. Taita Taveta: 78.69
7. Kitui: 77.55

🏆 Highest average Beans price: Turkana (110.47)
💰 Lowest average Beans price: Kitui (77.55)
------------------------------------------------------------

🌾 Ranking of counties by average Maize price:

24. Wajir: 39.90
11. Mandera: 39.62
2. Garissa: 39.05
22. Turkana: 36.99
3. Isiolo: 36.39
7. Kitui: 36.27
20. Taita Taveta: 36.17
18. Nyeri: 36.14
9. Machakos: 36.05
10. Makueni: 35.87
21. Tana River: 35.75
4. Kajiado: 35.71
16. Nairobi: 35.65
13. Meru North: 35.32
12. Marsabit: 34.99
14. Meru South: 34.13
23. Uasin Gishu: 33.53
6. Kisumu: 33.01
19. Samburu: 31.10
17. Nakuru: 30.20
5. Kilifi: 29.28
15. Mombasa: 28.99
8. Kwale: 28.89
25. West Pokot: 28.49
1. Baringo: 27.78

🏆 Highest average Maize price: Wajir (39.90)
💰 Lowest average Maize price: Baringo (27.78)
------------------------------------------------------------

🌾 Ranking of counties by average Potatoes_1kg price:

7. Kitui: 47.34
24. Wajir: 44.61
11. Mandera: 44.34
2. Garissa: 43.91
22. Turkana: 42.91
17. Nakuru: 42.82
4. Kajiado: 42.15
20. Taita Taveta: 42.06
3. Isiolo: 42.04
18. Nyeri: 42.03
9. Machakos: 41.97
10. Makueni: 41.89
16. Nairobi: 41.74
21. Tana River: 41.49
13. Meru North: 41.35
12. Marsabit: 40.95
14. Meru South: 40.49
6. Kisumu: 39.56
19. Samburu: 38.35
5. Kilifi: 36.54
8. Kwale: 36.35
15. Mombasa: 36.30
25. West Pokot: 35.92
1. Baringo: 35.39
23. Uasin Gishu: 30.95

🏆 Highest average Potatoes_1kg price: Kitui (47.34)
💰 Lowest average Potatoes_1kg price: Uasin Gishu (30.95)
------------------------------------------------------------

🌾 Ranking of counties by average Food_price_index price:

24. Wajir: 1.08
22. Turkana: 1.08
11. Mandera: 1.06
2. Garissa: 1.04
16. Nairobi: 1.01
12. Marsabit: 1.00
3. Isiolo: 0.98
18. Nyeri: 0.96
9. Machakos: 0.96
21. Tana River: 0.96
4. Kajiado: 0.95
5. Kilifi: 0.93
13. Meru North: 0.92
17. Nakuru: 0.92
6. Kisumu: 0.92
7. Kitui: 0.91
14. Meru South: 0.91
15. Mombasa: 0.91
25. West Pokot: 0.90
10. Makueni: 0.90
8. Kwale: 0.89
19. Samburu: 0.89
20. Taita Taveta: 0.89
23. Uasin Gishu: 0.84
1. Baringo: 0.84

🏆 Highest average Food_price_index price: Wajir (1.08)
💰 Lowest average Food_price_index price: Baringo (0.84)
------------------------------------------------------------

Regions that consistently have the highest or lowest food prices By Market¶

Average Closing Price for Food per Market¶

# Step 3: Group by market and year to get average closing prices per year
avg_prices_mkt = (
    df.groupby(['mkt_name','year'], as_index=False)[['beans_close', 'maize_close','potatoes_1kg_close','food_price_index_close']].mean()
)
avg_prices_mkt

Visualize the table above (Average Closing Price of Food Per Market)¶

# Step 1: Create a mapping of market → county & province
market_location_map = df[['mkt_name', 'counties', 'provinces']].drop_duplicates()

# Step 2: Group by market and year to compute averages (if not already done)
avg_prices_mkt = (
    df.groupby(['mkt_name', 'year'], as_index=False)[
        ['beans_close', 'maize_close', 'potatoes_1kg_close', 'food_price_index_close']
    ].mean()
)

# Step 3: Merge location info into the averages
avg_prices_mkt = avg_prices_mkt.merge(market_location_map, on='mkt_name', how='left')

# Step 4: Append county and province to market name
avg_prices_mkt['mkt_label'] = (
    avg_prices_mkt['mkt_name']
    + " (" 
    + avg_prices_mkt['counties'] 
    + ", " 
    + avg_prices_mkt['provinces'] 
    + ")"
)

# Step 5: Reshape data for plotting
price_columns = ['beans_close', 'maize_close', 'potatoes_1kg_close', 'food_price_index_close']
df_long = avg_prices_mkt.melt(
    id_vars=['mkt_label', 'year'],
    value_vars=price_columns,
    var_name='Commodity',
    value_name='Price'
)

print("--- Head of Reshaped (Long) Data ---")
print(df_long.head(), "\n")

# Step 6: Plot
sns.set_style("whitegrid")

g = sns.relplot(
    data=df_long,
    kind='line',
    x='year',
    y='Price',
    hue='Commodity',
    col='mkt_label',     # use new combined label
    col_wrap=3,
    height=3.5,
    aspect=1.5,
    legend='full'
)

g.set_axis_labels('Year', 'Average Closing Price')
g.set_titles(col_template='{col_name}')
g.fig.suptitle('Food Price Trends by Market (2007–2025)', fontsize=16, y=1.03)

plt.savefig('Market_price_trends_labeled.png', bbox_inches='tight')
print("Faceted plot saved to 'Market_price_trends_labeled.png'")

--- Head of Reshaped (Long) Data ---
                              mkt_label  year    Commodity      Price
0  Alango Arba (Garissa, North Eastern)  2007  beans_close  46.450833
1  Alango Arba (Garissa, North Eastern)  2008  beans_close  80.490833
2  Alango Arba (Garissa, North Eastern)  2009  beans_close  82.659167
3  Alango Arba (Garissa, North Eastern)  2010  beans_close  64.414167
4  Alango Arba (Garissa, North Eastern)  2011  beans_close  97.013333 

Faceted plot saved to 'Market_price_trends_labeled.png'

Markets with consistently the Highest & Lowest Food Price¶

# Created a New Column average_food_price per market
avg_prices_mkt['avg_food_price'] = avg_prices_mkt[
    ['beans_close', 'maize_close', 'potatoes_1kg_close']
].mean(axis=1)
avg_prices_mkt

Highest & Lowest Overall Average Food Prices By Market¶

# Compute the long-term average for each market
mkt_avg_overall = (
    avg_prices_mkt.groupby('mkt_name', as_index=False)['avg_food_price']
    .mean()
    .sort_values(by='avg_food_price', ascending=False)
)

# Display results
print(mkt_avg_overall)

# Get the top & bottom County
top_mkt = mkt_avg_overall.iloc[0]
bottom_mkt = mkt_avg_overall.iloc[-1]
print(f"Market with consistently highest food price: {top_mkt['mkt_name']} ({top_mkt['avg_food_price']:.2f})")
print(f"Market with consistently lowest food price: {bottom_mkt['mkt_name']} ({bottom_mkt['avg_food_price']:.2f})")

                   mkt_name  avg_food_price
103             Lodwar town       66.537321
64    Kalobeyei (Village 2)       66.192429
122               Loturerei       66.142083
32             Eliye Centre       66.116122
63    Kalobeyei (Village 1)       65.780203
..                      ...             ...
119           Lororo Centre       48.564512
101            Loboi Center       48.427539
93                  Koriema       48.419481
136  Marigat town (Baringo)       48.349804
81           Kimalel Center       48.313668

[184 rows x 2 columns]
Market with consistently highest food price: Lodwar town (66.54)
Market with consistently lowest food price: Kimalel Center (48.31)

Highest & Lowest Overall Prices by Market for each crop¶

# List of commodities to analyze
crops = ['beans_close', 'maize_close', 'potatoes_1kg_close', 'food_price_index_close']

for crop in crops:
    crop_name = crop.replace('_close', '').capitalize()
    print(f"\n🌾 Ranking of markets by average {crop_name} price:\n")

    # Compute average price per Market
    mkt_avg = (
        avg_prices_mkt.groupby('mkt_name', as_index=False)[crop]
        .mean()
        .sort_values(by=crop, ascending=False)
    )

    # Print full ranking
    for i, row in mkt_avg.iterrows():
        print(f"{i+1}. {row['mkt_name']}: {row[crop]:.2f}")

    # Identify top and bottom Market
    top = mkt_avg.iloc[0]
    bottom = mkt_avg.iloc[-1]

    # Print summary
    print(f"\n🏆 Highest average {crop_name} price: {top['mkt_name']} ({top[crop]:.2f})")
    print(f"💰 Lowest average {crop_name} price: {bottom['mkt_name']} ({bottom[crop]:.2f})")
    print("-" * 60)

🌾 Ranking of markets by average Beans price:

45. HongKong (Kakuma): 115.70
104. Lodwar town: 114.98
59. Kakuma 4: 114.28
35. Ethiopia (Kakuma): 114.22
119. Lopur: 114.14
144. Mogadishu (Kakuma): 113.98
58. Kakuma 3: 113.82
123. Loturerei: 113.81
33. Eliye Centre: 113.78
65. Kalobeyei (Village 2): 113.60
121. Lorugum: 113.60
156. Namoruputh: 113.26
110. Lokiriama: 112.90
62. Kalemunyang: 112.77
100. Letea: 112.62
67. Kalokol: 112.59
2. Alemsekon: 112.44
73. Kataboi: 112.24
64. Kalobeyei (Village 1): 112.16
57. Kakuma 2: 112.15
151. Naduat: 111.91
116. Lolupe: 111.88
106. Lokangae: 111.70
54. Kaeris: 111.47
124. Lowarengak: 111.03
111. Lokitaung: 111.01
63. Kaleng: 110.89
55. Kaikor: 110.71
52. Kaakelai: 110.71
158. Napak: 110.67
153. Nakinomet: 110.64
92. Kokuro: 110.58
66. Kalobeyei (Village 3): 110.40
179. Wajir town: 110.21
5. Aposta: 110.10
23. Dagahaley (Daadab): 109.79
107. Lokapel: 109.66
108. Lokichar (Turkana): 109.38
81. Kiliwaheri: 109.27
125. Loyangalani: 109.13
150. Naakalei: 109.00
109. Lokichoggio: 108.69
9. Banissa: 108.39
177. Tarbaj: 108.30
96. Lagbogol: 108.02
40. Griftu: 107.23
136. Marigat (Baringo): 106.63
71. Kargi: 105.93
42. Hadado: 105.79
19. Bute: 105.21
154. Nakukulas: 104.80
61. Kalemgorok: 104.77
77. Kibra (Nairobi): 104.66
30. Eldas: 104.33
29. El Wak: 104.22
41. Habaswein: 103.83
75. Katilia: 103.51
138. Marsabit: 103.25
28. Dub Goba: 103.24
139. Marsabit town: 103.14
32. Elelea: 103.08
76. Kawangware (Nairobi): 102.85
68. Kangemi (Nairobi): 102.78
27. Dirib: 102.74
167. Saretho: 102.52
180. Wakulima (Nairobi): 102.47
172. Songa: 102.35
152. Nairobi: 102.31
122. Lotubae: 102.22
88. Kitengela (Kajiado): 102.22
69. Karare: 102.02
140. Mathare (Nairobi): 101.83
165. Rhamu: 101.82
72. Kargi South Horr: 101.71
113. Lokwii: 101.29
11. Baraki: 101.23
147. Morulem: 101.21
3. Alinjugur: 101.14
112. Lokori: 101.13
6. Ashabito: 100.98
174. Takaba (Mandera): 100.90
1. Alango Arba: 100.84
134. Mandera town: 100.78
133. Mandera: 100.76
95. Korr: 100.61
24. Damajale: 100.48
143. Modogashe: 100.28
46. IFO (Daadab): 100.08
168. Sericho Center: 100.06
169. Shanta Abak: 99.95
166. Saka Town: 98.97
22. Dadaab town: 98.75
38. Garissa town (Garissa): 98.68
80. Kilifi: 98.50
37. Garissa: 98.48
26. Dertu: 98.41
48. Iresaboru Centre: 98.30
131. Malka GALLA: 98.17
25. Dandora (Nairobi): 97.94
127. Maalamin: 97.87
99. Lesirkan: 97.85
101. Liboi: 97.75
105. Loglogo: 97.07
50. Jarajara: 96.76
157. Nanigi: 96.16
149. Mukuru (Nairobi): 96.03
12. Benane: 95.97
56. Kajiado: 95.64
17. Bulesa Bulesa: 95.57
170. Shauri Yako: 95.51
87. Kisumu Ndogo (Mombasa): 95.49
43. Hagadera (Daadab): 95.48
14. Biliqo: 95.41
146. Moroto (Mombasa): 95.33
93. Kongowea (Mombasa): 95.27
142. Merti CENTER: 95.22
171. Shonda (Mombasa): 95.15
10. Baragoi: 95.13
60. Kalahari (Mombasa): 95.02
36. Garbatulla: 94.93
162. Ngurunit: 94.62
13. Bilbil: 94.56
132. Malkadaka: 94.50
16. Boka: 94.46
130. Makutano (West Pokot): 94.32
145. Mombasa: 94.25
117. Lomut (West Pokot): 94.03
20. Charidende: 93.62
51. Junda (Mombasa): 93.53
8. Bangladesh (Mombasa): 93.47
15. Boji: 93.45
18. Bura: 92.96
97. Laisamis Town: 92.82
178. Vanga (Kwale): 92.56
128. Makere: 91.74
70. Karatina (Nyeri): 91.42
141. Merille Town: 91.14
175. Tala Centre Market (Machakos): 90.67
44. Hola town: 90.43
155. Nakuru: 89.80
78. Kibuye (Kisumu): 89.24
181. Wakulima (Nakuru): 89.19
86. Kisumu: 88.72
39. Garsen: 88.36
183. Wayu: 88.20
176. Tarasaa: 88.16
83. Kipini: 87.99
160. Ngilai: 87.75
49. Isiolo town: 87.75
7. Bangale: 87.62
182. Wamba: 87.32
114. Lolkuniyani: 87.00
163. Oldonyiro: 86.80
91. Kiwanja: 86.69
164. Porro: 86.56
98. Lengusaka: 86.44
126. Lpus: 86.39
53. Kaanwa (Tharaka Nithi): 86.35
84. Kipsing CENTER: 86.30
103. Lodungokwe: 86.23
159. Ngaremara: 85.87
85. Kisima: 85.37
115. Lolkunono: 85.33
135. Maralal: 85.26
118. Loosuk: 84.93
21. Chemolingot: 84.85
173. Suguta: 84.64
31. Eldoret town (Uasin Gishu): 84.31
161. Nginyang: 83.98
34. Emali: 83.75
4. Amaya: 83.43
89. Kitui: 83.33
47. Illbissil Food Market (Kajiado): 83.02
94. Koriema: 82.69
120. Lororo Centre: 82.66
82. Kimalel Center: 82.25
102. Loboi Center: 82.21
137. Marigat town (Baringo): 82.04
79. Kibwezi: 81.00
184. Wote town (Makueni): 80.23
129. Makueni: 80.20
74. Kathonzweni (Makueni): 78.88
148. Mtito Andei: 78.69
90. Kitui town (Kitui): 71.77

🏆 Highest average Beans price: HongKong (Kakuma) (115.70)
💰 Lowest average Beans price: Kitui town (Kitui) (71.77)
------------------------------------------------------------

🌾 Ranking of markets by average Maize price:

43. Hagadera (Daadab): 41.70
179. Wajir town: 40.75
96. Lagbogol: 40.48
177. Tarbaj: 40.35
29. El Wak: 40.13
23. Dagahaley (Daadab): 40.06
64. Kalobeyei (Village 1): 39.92
40. Griftu: 39.90
65. Kalobeyei (Village 2): 39.81
46. IFO (Daadab): 39.66
104. Lodwar town: 39.66
123. Loturerei: 39.65
9. Banissa: 39.63
33. Eliye Centre: 39.63
41. Habaswein: 39.62
6. Ashabito: 39.60
81. Kiliwaheri: 39.59
133. Mandera: 39.57
134. Mandera town: 39.56
165. Rhamu: 39.48
24. Damajale: 39.44
22. Dadaab town: 39.42
3. Alinjugur: 39.39
42. Hadado: 39.38
19. Bute: 39.37
167. Saretho: 39.35
174. Takaba (Mandera): 39.35
30. Eldas: 39.34
11. Baraki: 39.21
38. Garissa town (Garissa): 39.10
37. Garissa: 38.99
1. Alango Arba: 38.80
143. Modogashe: 38.80
67. Kalokol: 38.73
100. Letea: 38.55
169. Shanta Abak: 38.47
157. Nanigi: 38.46
62. Kalemunyang: 38.43
121. Lorugum: 38.37
73. Kataboi: 38.36
127. Maalamin: 38.36
101. Liboi: 38.26
168. Sericho Center: 38.15
25. Dandora (Nairobi): 38.14
66. Kalobeyei (Village 3): 38.09
13. Bilbil: 38.07
18. Bura: 37.90
50. Jarajara: 37.71
156. Namoruputh: 37.68
124. Lowarengak: 37.64
166. Saka Town: 37.59
12. Benane: 37.51
26. Dertu: 37.51
36. Garbatulla: 37.43
7. Bangale: 37.40
111. Lokitaung: 37.40
48. Iresaboru Centre: 37.31
116. Lolupe: 37.24
110. Lokiriama: 37.22
54. Kaeris: 37.21
15. Boji: 37.18
144. Mogadishu (Kakuma): 37.17
151. Naduat: 37.16
150. Naakalei: 37.14
128. Makere: 37.13
131. Malka GALLA: 37.06
35. Ethiopia (Kakuma): 37.02
125. Loyangalani: 36.93
92. Kokuro: 36.93
108. Lokichar (Turkana): 36.90
63. Kaleng: 36.89
52. Kaakelai: 36.81
68. Kangemi (Nairobi): 36.73
45. HongKong (Kakuma): 36.72
107. Lokapel: 36.71
76. Kawangware (Nairobi): 36.67
58. Kakuma 3: 36.67
16. Boka: 36.59
55. Kaikor: 36.46
2. Alemsekon: 36.43
142. Merti CENTER: 36.40
153. Nakinomet: 36.35
158. Napak: 36.35
90. Kitui town (Kitui): 36.28
89. Kitui: 36.26
57. Kakuma 2: 36.22
77. Kibra (Nairobi): 36.18
148. Mtito Andei: 36.17
183. Wayu: 36.17
74. Kathonzweni (Makueni): 36.15
70. Karatina (Nyeri): 36.14
119. Lopur: 36.13
175. Tala Centre Market (Machakos): 36.05
154. Nakukulas: 36.03
59. Kakuma 4: 36.02
17. Bulesa Bulesa: 36.00
184. Wote town (Makueni): 35.99
44. Hola town: 35.99
56. Kajiado: 35.98
129. Makueni: 35.98
132. Malkadaka: 35.96
79. Kibwezi: 35.89
14. Biliqo: 35.85
86. Kisumu: 35.78
71. Kargi: 35.76
106. Lokangae: 35.76
75. Katilia: 35.70
28. Dub Goba: 35.60
27. Dirib: 35.59
61. Kalemgorok: 35.54
138. Marsabit: 35.51
32. Elelea: 35.49
159. Ngaremara: 35.48
149. Mukuru (Nairobi): 35.46
47. Illbissil Food Market (Kajiado): 35.44
139. Marsabit town: 35.44
5. Aposta: 35.42
172. Songa: 35.38
34. Emali: 35.34
91. Kiwanja: 35.15
69. Karare: 35.14
49. Isiolo town: 35.12
122. Lotubae: 35.04
147. Morulem: 34.81
113. Lokwii: 34.80
105. Loglogo: 34.76
180. Wakulima (Nairobi): 34.74
109. Lokichoggio: 34.71
112. Lokori: 34.61
140. Mathare (Nairobi): 34.61
88. Kitengela (Kajiado): 34.25
95. Korr: 34.18
53. Kaanwa (Tharaka Nithi): 34.13
84. Kipsing CENTER: 34.10
152. Nairobi: 34.09
97. Laisamis Town: 33.80
141. Merille Town: 33.69
31. Eldoret town (Uasin Gishu): 33.53
39. Garsen: 33.24
83. Kipini: 33.13
176. Tarasaa: 33.10
72. Kargi South Horr: 33.10
182. Wamba: 33.03
98. Lengusaka: 32.92
162. Ngurunit: 32.84
114. Lolkuniyani: 32.68
20. Charidende: 32.64
160. Ngilai: 32.59
163. Oldonyiro: 32.55
145. Mombasa: 32.40
155. Nakuru: 32.22
126. Lpus: 31.68
103. Lodungokwe: 31.68
99. Lesirkan: 31.41
10. Baragoi: 31.11
51. Junda (Mombasa): 30.72
78. Kibuye (Kisumu): 30.24
85. Kisima: 30.14
135. Maralal: 29.53
164. Porro: 29.36
80. Kilifi: 29.28
115. Lolkunono: 29.27
118. Loosuk: 29.14
173. Suguta: 29.14
178. Vanga (Kwale): 28.89
117. Lomut (West Pokot): 28.79
8. Bangladesh (Mombasa): 28.50
4. Amaya: 28.46
60. Kalahari (Mombasa): 28.41
170. Shauri Yako: 28.34
87. Kisumu Ndogo (Mombasa): 28.25
146. Moroto (Mombasa): 28.25
130. Makutano (West Pokot): 28.18
181. Wakulima (Nakuru): 28.17
171. Shonda (Mombasa): 28.03
93. Kongowea (Mombasa): 28.02
161. Nginyang: 27.87
21. Chemolingot: 27.80
102. Loboi Center: 27.72
120. Lororo Centre: 27.72
137. Marigat town (Baringo): 27.71
136. Marigat (Baringo): 27.70
82. Kimalel Center: 27.56
94. Koriema: 27.51

🏆 Highest average Maize price: Hagadera (Daadab) (41.70)
💰 Lowest average Maize price: Koriema (27.51)
------------------------------------------------------------

🌾 Ranking of markets by average Potatoes_1kg price:

89. Kitui: 52.50
155. Nakuru: 49.91
43. Hagadera (Daadab): 45.97
64. Kalobeyei (Village 1): 45.27
179. Wajir town: 45.27
65. Kalobeyei (Village 2): 45.16
96. Lagbogol: 45.02
104. Lodwar town: 44.98
123. Loturerei: 44.96
33. Eliye Centre: 44.94
177. Tarbaj: 44.94
29. El Wak: 44.77
40. Griftu: 44.64
23. Dagahaley (Daadab): 44.46
41. Habaswein: 44.40
9. Banissa: 44.33
6. Ashabito: 44.33
133. Mandera: 44.31
134. Mandera town: 44.31
81. Kiliwaheri: 44.31
67. Kalokol: 44.26
165. Rhamu: 44.24
42. Hadado: 44.22
46. IFO (Daadab): 44.21
30. Eldas: 44.19
19. Bute: 44.18
3. Alinjugur: 44.17
24. Damajale: 44.17
22. Dadaab town: 44.15
167. Saretho: 44.15
174. Takaba (Mandera): 44.15
100. Letea: 44.13
62. Kalemunyang: 44.07
11. Baraki: 44.03
121. Lorugum: 44.00
73. Kataboi: 43.97
66. Kalobeyei (Village 3): 43.85
38. Garissa town (Garissa): 43.84
1. Alango Arba: 43.79
37. Garissa: 43.78
143. Modogashe: 43.70
169. Shanta Abak: 43.63
25. Dandora (Nairobi): 43.53
101. Liboi: 43.50
156. Namoruputh: 43.48
127. Maalamin: 43.48
157. Nanigi: 43.40
124. Lowarengak: 43.38
168. Sericho Center: 43.31
13. Bilbil: 43.20
111. Lokitaung: 43.20
116. Lolupe: 43.12
110. Lokiriama: 43.11
151. Naduat: 43.07
54. Kaeris: 43.06
18. Bura: 43.04
50. Jarajara: 42.95
150. Naakalei: 42.94
166. Saka Town: 42.91
144. Mogadishu (Kakuma): 42.89
26. Dertu: 42.86
92. Kokuro: 42.84
108. Lokichar (Turkana): 42.84
12. Benane: 42.84
63. Kaleng: 42.83
35. Ethiopia (Kakuma): 42.78
7. Bangale: 42.76
52. Kaakelai: 42.76
36. Garbatulla: 42.75
48. Iresaboru Centre: 42.73
107. Lokapel: 42.71
125. Loyangalani: 42.60
45. HongKong (Kakuma): 42.57
15. Boji: 42.56
131. Malka GALLA: 42.54
58. Kakuma 3: 42.54
55. Kaikor: 42.48
2. Alemsekon: 42.47
128. Makere: 42.45
68. Kangemi (Nairobi): 42.44
76. Kawangware (Nairobi): 42.42
158. Napak: 42.40
153. Nakinomet: 42.40
57. Kakuma 2: 42.28
56. Kajiado: 42.24
154. Nakukulas: 42.21
119. Lopur: 42.21
16. Boka: 42.19
90. Kitui town (Kitui): 42.19
59. Kakuma 4: 42.15
74. Kathonzweni (Makueni): 42.09
148. Mtito Andei: 42.06
142. Merti CENTER: 42.06
47. Illbissil Food Market (Kajiado): 42.05
86. Kisumu: 42.04
70. Karatina (Nyeri): 42.03
184. Wote town (Makueni): 42.01
129. Makueni: 42.00
77. Kibra (Nairobi): 42.00
106. Lokangae: 41.98
175. Tala Centre Market (Machakos): 41.97
75. Katilia: 41.91
5. Aposta: 41.87
61. Kalemgorok: 41.85
79. Kibwezi: 41.83
17. Bulesa Bulesa: 41.76
132. Malkadaka: 41.74
32. Elelea: 41.74
183. Wayu: 41.69
14. Biliqo: 41.65
44. Hola town: 41.58
71. Kargi: 41.57
34. Emali: 41.49
159. Ngaremara: 41.48
149. Mukuru (Nairobi): 41.40
122. Lotubae: 41.38
109. Lokichoggio: 41.37
28. Dub Goba: 41.35
27. Dirib: 41.34
152. Nairobi: 41.32
138. Marsabit: 41.32
139. Marsabit town: 41.26
91. Kiwanja: 41.22
147. Morulem: 41.20
113. Lokwii: 41.20
172. Songa: 41.14
49. Isiolo town: 41.14
112. Lokori: 41.08
69. Karare: 41.00
180. Wakulima (Nairobi): 40.99
140. Mathare (Nairobi): 40.91
105. Loglogo: 40.72
88. Kitengela (Kajiado): 40.69
84. Kipsing CENTER: 40.50
53. Kaanwa (Tharaka Nithi): 40.49
95. Korr: 40.35
97. Laisamis Town: 40.04
141. Merille Town: 40.01
182. Wamba: 39.92
98. Lengusaka: 39.86
114. Lolkuniyani: 39.66
72. Kargi South Horr: 39.66
39. Garsen: 39.65
162. Ngurunit: 39.58
160. Ngilai: 39.58
83. Kipini: 39.55
176. Tarasaa: 39.55
163. Oldonyiro: 39.34
20. Charidende: 39.27
103. Lodungokwe: 38.92
126. Lpus: 38.91
99. Lesirkan: 38.51
10. Baragoi: 38.32
145. Mombasa: 38.02
51. Junda (Mombasa): 37.80
85. Kisima: 37.69
78. Kibuye (Kisumu): 37.09
135. Maralal: 37.06
164. Porro: 36.91
115. Lolkunono: 36.83
173. Suguta: 36.82
118. Loosuk: 36.73
80. Kilifi: 36.54
178. Vanga (Kwale): 36.35
117. Lomut (West Pokot): 36.25
4. Amaya: 36.14
8. Bangladesh (Mombasa): 36.02
60. Kalahari (Mombasa): 35.95
170. Shauri Yako: 35.93
87. Kisumu Ndogo (Mombasa): 35.86
146. Moroto (Mombasa): 35.84
181. Wakulima (Nakuru): 35.74
93. Kongowea (Mombasa): 35.66
171. Shonda (Mombasa): 35.65
130. Makutano (West Pokot): 35.60
161. Nginyang: 35.48
21. Chemolingot: 35.41
102. Loboi Center: 35.35
136. Marigat (Baringo): 35.33
120. Lororo Centre: 35.32
137. Marigat town (Baringo): 35.30
82. Kimalel Center: 35.13
94. Koriema: 35.06
31. Eldoret town (Uasin Gishu): 30.95

🏆 Highest average Potatoes_1kg price: Kitui (52.50)
💰 Lowest average Potatoes_1kg price: Eldoret town (Uasin Gishu) (30.95)
------------------------------------------------------------

🌾 Ranking of markets by average Food_price_index price:

104. Lodwar town: 1.13
65. Kalobeyei (Village 2): 1.13
123. Loturerei: 1.13
33. Eliye Centre: 1.12
64. Kalobeyei (Village 1): 1.12
179. Wajir town: 1.11
121. Lorugum: 1.11
67. Kalokol: 1.11
62. Kalemunyang: 1.11
100. Letea: 1.11
45. HongKong (Kakuma): 1.11
73. Kataboi: 1.10
156. Namoruputh: 1.10
23. Dagahaley (Daadab): 1.10
144. Mogadishu (Kakuma): 1.10
35. Ethiopia (Kakuma): 1.10
177. Tarbaj: 1.10
96. Lagbogol: 1.10
110. Lokiriama: 1.10
81. Kiliwaheri: 1.10
58. Kakuma 3: 1.09
119. Lopur: 1.09
59. Kakuma 4: 1.09
9. Banissa: 1.09
66. Kalobeyei (Village 3): 1.09
116. Lolupe: 1.09
151. Naduat: 1.09
124. Lowarengak: 1.09
54. Kaeris: 1.09
40. Griftu: 1.09
111. Lokitaung: 1.09
2. Alemsekon: 1.08
57. Kakuma 2: 1.08
63. Kaleng: 1.08
92. Kokuro: 1.08
52. Kaakelai: 1.08
55. Kaikor: 1.08
106. Lokangae: 1.07
158. Napak: 1.07
42. Hadado: 1.07
153. Nakinomet: 1.07
108. Lokichar (Turkana): 1.07
107. Lokapel: 1.07
29. El Wak: 1.07
150. Naakalei: 1.07
19. Bute: 1.07
125. Loyangalani: 1.07
30. Eldas: 1.07
41. Habaswein: 1.06
5. Aposta: 1.06
167. Saretho: 1.05
165. Rhamu: 1.05
6. Ashabito: 1.05
109. Lokichoggio: 1.05
3. Alinjugur: 1.05
134. Mandera town: 1.05
133. Mandera: 1.05
11. Baraki: 1.05
174. Takaba (Mandera): 1.05
24. Damajale: 1.04
46. IFO (Daadab): 1.04
1. Alango Arba: 1.04
71. Kargi: 1.04
43. Hagadera (Daadab): 1.04
154. Nakukulas: 1.04
77. Kibra (Nairobi): 1.04
143. Modogashe: 1.04
22. Dadaab town: 1.03
61. Kalemgorok: 1.03
169. Shanta Abak: 1.03
68. Kangemi (Nairobi): 1.03
76. Kawangware (Nairobi): 1.03
38. Garissa town (Garissa): 1.03
168. Sericho Center: 1.03
37. Garissa: 1.03
75. Katilia: 1.03
32. Elelea: 1.02
28. Dub Goba: 1.02
138. Marsabit: 1.02
139. Marsabit town: 1.02
27. Dirib: 1.02
127. Maalamin: 1.02
25. Dandora (Nairobi): 1.02
101. Liboi: 1.02
166. Saka Town: 1.02
172. Songa: 1.01
26. Dertu: 1.01
122. Lotubae: 1.01
48. Iresaboru Centre: 1.01
180. Wakulima (Nairobi): 1.01
69. Karare: 1.01
157. Nanigi: 1.01
131. Malka GALLA: 1.01
152. Nairobi: 1.01
50. Jarajara: 1.01
140. Mathare (Nairobi): 1.01
113. Lokwii: 1.01
147. Morulem: 1.00
88. Kitengela (Kajiado): 1.00
112. Lokori: 1.00
12. Benane: 1.00
13. Bilbil: 1.00
95. Korr: 0.99
36. Garbatulla: 0.99
72. Kargi South Horr: 0.99
18. Bura: 0.99
56. Kajiado: 0.99
142. Merti CENTER: 0.98
17. Bulesa Bulesa: 0.98
16. Boka: 0.98
15. Boji: 0.98
14. Biliqo: 0.98
149. Mukuru (Nairobi): 0.98
105. Loglogo: 0.98
132. Malkadaka: 0.98
89. Kitui: 0.98
155. Nakuru: 0.97
128. Makere: 0.97
136. Marigat (Baringo): 0.96
70. Karatina (Nyeri): 0.96
175. Tala Centre Market (Machakos): 0.96
44. Hola town: 0.95
7. Bangale: 0.95
99. Lesirkan: 0.95
162. Ngurunit: 0.95
97. Laisamis Town: 0.94
86. Kisumu: 0.94
183. Wayu: 0.94
20. Charidende: 0.94
141. Merille Town: 0.93
145. Mombasa: 0.93
10. Baragoi: 0.93
80. Kilifi: 0.93
49. Isiolo town: 0.93
91. Kiwanja: 0.92
159. Ngaremara: 0.92
51. Junda (Mombasa): 0.92
39. Garsen: 0.91
53. Kaanwa (Tharaka Nithi): 0.91
84. Kipsing CENTER: 0.91
176. Tarasaa: 0.91
83. Kipini: 0.91
34. Emali: 0.91
47. Illbissil Food Market (Kajiado): 0.91
182. Wamba: 0.91
160. Ngilai: 0.91
170. Shauri Yako: 0.91
87. Kisumu Ndogo (Mombasa): 0.90
146. Moroto (Mombasa): 0.90
114. Lolkuniyani: 0.90
60. Kalahari (Mombasa): 0.90
98. Lengusaka: 0.90
117. Lomut (West Pokot): 0.90
93. Kongowea (Mombasa): 0.90
171. Shonda (Mombasa): 0.90
163. Oldonyiro: 0.90
79. Kibwezi: 0.90
184. Wote town (Makueni): 0.90
129. Makueni: 0.90
130. Makutano (West Pokot): 0.90
8. Bangladesh (Mombasa): 0.90
178. Vanga (Kwale): 0.89
74. Kathonzweni (Makueni): 0.89
126. Lpus: 0.89
148. Mtito Andei: 0.89
103. Lodungokwe: 0.89
78. Kibuye (Kisumu): 0.89
85. Kisima: 0.87
181. Wakulima (Nakuru): 0.87
164. Porro: 0.87
135. Maralal: 0.86
115. Lolkunono: 0.86
118. Loosuk: 0.86
173. Suguta: 0.85
90. Kitui town (Kitui): 0.85
31. Eldoret town (Uasin Gishu): 0.84
21. Chemolingot: 0.84
4. Amaya: 0.84
161. Nginyang: 0.84
120. Lororo Centre: 0.83
102. Loboi Center: 0.82
94. Koriema: 0.82
137. Marigat town (Baringo): 0.82
82. Kimalel Center: 0.82

🏆 Highest average Food_price_index price: Lodwar town (1.13)
💰 Lowest average Food_price_index price: Kimalel Center (0.82)
------------------------------------------------------------

Insights From the Charts¶

1. Universal Price Shock (2022-2023)¶

The most striking feature across every single province and county is a dramatic and sharp increase in food prices starting around 2022 and peaking in 2023.

Nationwide Event: The fact that this spike occurs simultaneously in all regions (from Wajir in the North Eastern to Mombasa on the Coast, and Nakuru in the Rift Valley) strongly suggests a nationwide event, such as a severe drought, major economic factors (e.g., inflation, fuel prices), or global supply chain issues affecting the entire country.

Gradual Rise Pre-2020: Before this spike, the general trend from 2007 to 2020 was a slow, gradual increase in prices for all commodities.

Post-Peak Leveling: The data from 2023 towards 2025 indicates that prices have begun to fall slightly or level off from their extreme 2023 peaks, though they remain significantly higher than pre-2022 levels.

2. Commodity-Specific Price Tiers¶

The commodities exist in distinct price brackets:

Beans (beans_close): This is consistently the most expensive commodity by a significant margin in all regions. It also exhibits the highest volatility, with the most dramatic peaks and troughs, and it experienced the most extreme price surge during the 2022-2023 event.

Maize (maize_close) and Potatoes (potatoes_1kg_close): These two commodities are much cheaper than beans. Their prices track each other very closely across the entire 18-year period and in all regions. This high correlation suggests their prices are driven by similar market forces, and they may act as substitutes for one another.

Food Price Index (food_price_index_close): This red line remains consistently flat and low (around a value of 10-15) in every chart. This indicates it is not a direct price but likely a composite index measured against a base year, and it is not as volatile as the prices of these individual staple foods.

3. Regional Variations in Price Magnitude¶

While all regions follow the same pattern, the magnitude of the price spikes varies.

Highest Price Spikes: The provincial chart shows that the North Eastern, Coast, and Eastern provinces experienced the most severe price peaks for beans (beans_close), with average prices surging well above 150.

Supporting County Data: This is confirmed by the county-level charts. For example, Wajir (North Eastern) and Mombasa (Coast) show some of the most extreme peaks in bean prices.

Major Production Areas: Regions like the Rift Valley (e.g., Nakuru, Uasin Gishu counties) also saw severe price hikes, but the peak for beans appears to be slightly less extreme than in the North Eastern or Coast regions. This could imply that regions further from production centers or with logistical challenges (like North Eastern) face greater price volatility.

Inflation & Volatility¶

So for this phase, we will check out the inflation across the years for each food price, by province that is, which province has the most volatile inflation prices, then we'll double up on county and market. For the visuals, maybe a bar graph

Visualizing Inflation by Time (Yearly & Monthly)¶

yearly_trends = df.groupby('year')[['inflation_beans','inflation_maize','inflation_potatoes','inflation_food_price_index']].mean().reset_index()
yearly_trends

Beans Inflation over the years (2008 - 2025)¶

# Filter out 2007 and enhance the visualization
sns.set_theme(style='whitegrid')

plt.figure(figsize=(12, 6))

# Filter data to remove 2007 and get non-NaN values
filtered_data = yearly_trends[
    (yearly_trends['year'] > 2007) & 
    (yearly_trends['inflation_beans'].notna())
]

# Create the bar plot
bars = sns.barplot(
    data=filtered_data,
    x='year',
    y='inflation_beans',
    color='#3498db',       # Blue for positive values
    edgecolor='#2980b9'    # Darker blue for edges
)

# Color negative bars in red
for i, bar in enumerate(bars.patches):
    if bar.get_height() < 0:
        bar.set_color('#e74c3c')    # Red for negative values
        bar.set_edgecolor('#c0392b') # Darker red for edges

# Enhance the visual style
plt.title('Beans Inflation in Kenya (2008–2025)', 
          fontsize=16, 
          pad=20,
          weight='bold')
plt.xlabel('Year', fontsize=12)
plt.ylabel('Inflation', fontsize=12)

# Customize grid
plt.grid(True, linestyle='--', alpha=0.3, axis='y')

# Rotate x-axis labels for better readability
plt.xticks(rotation=45)

# Add value labels on top/bottom of each bar
for i, row in enumerate(filtered_data.itertuples()):
    if pd.notna(row.inflation_beans):
        value = row.inflation_beans
        va = 'bottom' if value >= 0 else 'top'  # Adjust label position based on value
        plt.text(i, value, 
                f'{value:.1f}', 
                ha='center', 
                va=va,
                fontsize=10)

# Adjust layout
plt.tight_layout()

# Set y-axis limits with padding for both positive and negative values
valid_values = filtered_data['inflation_beans'].dropna()
if not valid_values.empty:
    max_val = max(valid_values)
    min_val = min(valid_values)
    plt.ylim(min_val * 1.1, max_val * 1.1)  # Add 10% padding on both ends

# Add horizontal line at y=0
plt.axhline(y=0, color='black', linestyle='-', linewidth=0.5, alpha=0.3)

plt.show()

Maize Inflation over the years (2008 - 2025)¶

# Filter out 2007 and enhance the visualization
sns.set_theme(style='whitegrid')

plt.figure(figsize=(12, 6))

# Filter data to remove 2007 and get non-NaN values
filtered_data = yearly_trends[
    (yearly_trends['year'] > 2007) & 
    (yearly_trends['inflation_maize'].notna())
]

# Create the bar plot
bars = sns.barplot(
    data=filtered_data,
    x='year',
    y='inflation_maize',
    color='#3498db',       # Blue for positive values
    edgecolor='#2980b9'    # Darker blue for edges
)

# Color negative bars in red
for i, bar in enumerate(bars.patches):
    if bar.get_height() < 0:
        bar.set_color('#e74c3c')    # Red for negative values
        bar.set_edgecolor('#c0392b') # Darker red for edges

# Enhance the visual style
plt.title('Maize Inflation in Kenya (2008–2025)', 
          fontsize=16, 
          pad=20,
          weight='bold')
plt.xlabel('Year', fontsize=12)
plt.ylabel('Inflation', fontsize=12)

# Customize grid
plt.grid(True, linestyle='--', alpha=0.3, axis='y')

# Rotate x-axis labels for better readability
plt.xticks(rotation=45)

# Add value labels on top/bottom of each bar
for i, row in enumerate(filtered_data.itertuples()):
    if pd.notna(row.inflation_maize):
        value = row.inflation_maize
        va = 'bottom' if value >= 0 else 'top'  # Adjust label position based on value
        plt.text(i, value, 
                f'{value:.1f}', 
                ha='center', 
                va=va,
                fontsize=10)

# Adjust layout
plt.tight_layout()

# Set y-axis limits with padding for both positive and negative values
valid_values = filtered_data['inflation_maize'].dropna()
if not valid_values.empty:
    max_val = max(valid_values)
    min_val = min(valid_values)
    plt.ylim(min_val * 1.1, max_val * 1.1)  # Add 10% padding on both ends

# Add horizontal line at y=0
plt.axhline(y=0, color='black', linestyle='-', linewidth=0.5, alpha=0.3)

plt.show()

Potatoes Inflation over the years (2008 - 2025)¶

# Filter out 2007 and enhance the visualization
sns.set_theme(style='whitegrid')

plt.figure(figsize=(12, 6))

# Filter data to remove 2007 and get non-NaN values
filtered_data = yearly_trends[
    (yearly_trends['year'] > 2007) & 
    (yearly_trends['inflation_potatoes'].notna())
]

# Create the bar plot
bars = sns.barplot(
    data=filtered_data,
    x='year',
    y='inflation_potatoes',
    color='#3498db',       # Blue for positive values
    edgecolor='#2980b9'    # Darker blue for edges
)

# Color negative bars in red
for i, bar in enumerate(bars.patches):
    if bar.get_height() < 0:
        bar.set_color('#e74c3c')    # Red for negative values
        bar.set_edgecolor('#c0392b') # Darker red for edges

# Enhance the visual style
plt.title('Potatoes Inflation in Kenya (2008–2025)', 
          fontsize=16, 
          pad=20,
          weight='bold')
plt.xlabel('Year', fontsize=12)
plt.ylabel('Inflation', fontsize=12)

# Customize grid
plt.grid(True, linestyle='--', alpha=0.3, axis='y')

# Rotate x-axis labels for better readability
plt.xticks(rotation=45)

# Add value labels on top/bottom of each bar
for i, row in enumerate(filtered_data.itertuples()):
    if pd.notna(row.inflation_potatoes):
        value = row.inflation_potatoes
        va = 'bottom' if value >= 0 else 'top'  # Adjust label position based on value
        plt.text(i, value, 
                f'{value:.1f}', 
                ha='center', 
                va=va,
                fontsize=10)

# Adjust layout
plt.tight_layout()

# Set y-axis limits with padding for both positive and negative values
valid_values = filtered_data['inflation_potatoes'].dropna()
if not valid_values.empty:
    max_val = max(valid_values)
    min_val = min(valid_values)
    plt.ylim(min_val * 1.1, max_val * 1.1)  # Add 10% padding on both ends

# Add horizontal line at y=0
plt.axhline(y=0, color='black', linestyle='-', linewidth=0.5, alpha=0.3)

plt.show()

Inflation Trends over the years (2008 - 2025)¶

# Filter out 2007 and enhance the visualization
sns.set_theme(style='whitegrid')

plt.figure(figsize=(12, 6))

# Filter data to remove 2007 and get non-NaN values
filtered_data = yearly_trends[
    (yearly_trends['year'] > 2007) & 
    (yearly_trends['inflation_food_price_index'].notna())
]

# Create the bar plot
bars = sns.barplot(
    data=filtered_data,
    x='year',
    y='inflation_food_price_index',
    color='#3498db',       # Blue for positive values
    edgecolor='#2980b9'    # Darker blue for edges
)

# Color negative bars in red
for i, bar in enumerate(bars.patches):
    if bar.get_height() < 0:
        bar.set_color('#e74c3c')    # Red for negative values
        bar.set_edgecolor('#c0392b') # Darker red for edges

# Enhance the visual style
plt.title('Food Price Inflation in Kenya (2008–2025)', 
          fontsize=16, 
          pad=20,
          weight='bold')
plt.xlabel('Year', fontsize=12)
plt.ylabel('Inflation', fontsize=12)

# Customize grid
plt.grid(True, linestyle='--', alpha=0.3, axis='y')

# Rotate x-axis labels for better readability
plt.xticks(rotation=45)

# Add value labels on top/bottom of each bar
for i, row in enumerate(filtered_data.itertuples()):
    if pd.notna(row.inflation_food_price_index):
        value = row.inflation_food_price_index
        va = 'bottom' if value >= 0 else 'top'  # Adjust label position based on value
        plt.text(i, value, 
                f'{value:.1f}', 
                ha='center', 
                va=va,
                fontsize=10)

# Adjust layout
plt.tight_layout()

# Set y-axis limits with padding for both positive and negative values
valid_values = filtered_data['inflation_food_price_index'].dropna()
if not valid_values.empty:
    max_val = max(valid_values)
    min_val = min(valid_values)
    plt.ylim(min_val * 1.1, max_val * 1.1)  # Add 10% padding on both ends

# Add horizontal line at y=0
plt.axhline(y=0, color='black', linestyle='-', linewidth=0.5, alpha=0.3)

plt.show()

A line Graph to show the Inflation of all commodities over Time¶

plt.figure(figsize=(12, 6))
sns.lineplot(data=yearly_trends, x='year', y='inflation_beans', label='Beans')
sns.lineplot(data=yearly_trends, x='year', y='inflation_maize', label='Maize')
sns.lineplot(data=yearly_trends, x='year', y='inflation_potatoes', label='Potatoes')
sns.lineplot(data=yearly_trends, x='year', y='inflation_food_price_index', label='Food_Price_Index')

plt.title('Overall Inflation of Food Price Trends in Kenya (2007–2025)', fontsize=14)
plt.xlabel('Year')
plt.ylabel('Inflation')
plt.legend(title='Commodity')
plt.grid(True, linestyle='--', alpha=0.5)
plt.tight_layout()
plt.show()

1. Beans¶

Starts high (~75% inflation around 2008), drops sharply around 2010, then spikes again.

Shows multiple large swings, especially between 2008–2011 and 2018–2023.

Overall: Significant but less extreme than Maize.

2. Maize¶

Shows the largest upward spikes in multiple years — particularly around 2011 and 2023, where inflation appears to exceed 80%.

Also dips deeply into the negatives during deflation periods (e.g., around 2010 and 2018).

This suggests the greatest volatility and strong inflation bursts over time.

3. Potatoes¶

Experiences moderate fluctuations that largely track with overall inflation trends.

Less extreme peaks and troughs compared to Beans or Maize.

More stable relative to the other commodities.

4. Food Price Index (FPI)¶

Averages out the volatility — it’s smoother because it’s an aggregate measure.

📊 Conclusion¶

Commodity	Inflation Volatility	Description
Maize	🔺 Highest	Sharpest spikes and dips — most volatile inflation trend
Beans	⚖️ Moderate	Noticeable swings but less extreme
Potatoes	🔻 Lowest	More stable trend, closely follows FPI

Heat map for Inlation Overtime¶

monthly_trends_inflation = df.groupby(['year','month'])[['inflation_beans','inflation_maize','inflation_potatoes','inflation_food_price_index']].mean().reset_index()
monthly_trends_inflation

# Make sure month names are properly formatted and ordered
month_order = list(calendar.month_name[1:])

# Convert month column to an ordered categorical type
monthly_trends['month'] = pd.Categorical(monthly_trends['month'],
                                         categories=month_order,
                                         ordered=True)

# pivot will respect that order
pivot = monthly_trends_inflation.pivot(index='year', columns='month', values='inflation_food_price_index')

plt.figure(figsize=(10,6))
sns.heatmap(pivot, cmap='RdYlGn_r', annot=True, fmt=".1f")
plt.title('Seasonal Heatmap of Inflation (Monthly Averages)')
plt.xlabel('Month')
plt.ylabel('Year')
plt.show()

🌡️ Seasonal Heatmap of Inflation (Monthly Averages, 2007–2025)¶

The heatmap visualizes monthly inflation variations across years, showing how inflation fluctuates seasonally and historically.

🟥 1. High-Inflation Periods (Hot Zones)¶

2011 stands out with extreme inflation — especially June to September (75–105), making it the most inflationary year overall.
Other notable spikes include:
- 2008 (June–September) — early post-election economic shock.
- 2023 (April–August) — resurgence of strong inflation across mid-year months.
These months often correspond to harvest delays, food shortages, or high import dependency.

🟩 2. Low-Inflation / Deflation Periods (Cool Zones)¶

Significant deflation or low inflation occurred in:
- 2010 and 2018–2019, especially between March and October, with rates as low as –42.
- 2025 (projected) also shows a mild negative inflation trend.
These likely align with economic stabilization periods, bumper harvests, or policy interventions that reduced price pressures.

🟨 3. Seasonal Trends¶

Inflation typically rises during mid-year (June–August) and dips toward the end of the year (October–December).
This pattern suggests cyclical price pressures tied to agriculture and supply chain fluctuations — when food production is low, prices rise; when harvests peak, prices fall.

🧩 4. Long-Term Insights¶

The most volatile decade: 2008–2013, characterized by high spikes and steep drops.
The most stable years: 2014–2017 and parts of 2019–2022, where inflation fluctuates within a narrower range.
Post-2022 trends show inflation volatility returning, indicating renewed market pressure.

📊 Summary Table¶

Period	Inflation Behavior	Description
2008–2011	🔺 Very High	Inflation shocks, peaking in 2011
2012–2017	⚖️ Moderate	Relative stabilization with occasional rises
2018–2019	🔻 Low/Negative	Deflation periods across multiple months
2020–2022	⚖️ Moderate	Recovery years with slight upward trends
2023–2025	🔺 Rising Again	Renewed inflation volatility, mid-year peaks

🧠 Insight:
Kenya’s inflation demonstrates strong seasonal dependence and external shock sensitivity. The mid-year months (June–August) remain the most inflation-prone, likely tied to agricultural cycles and import-driven costs.

Let's Check Inflation by Province¶

# Group inflation data by province and year
province_inflation = (
    df.groupby(['provinces', 'year'])[
        ['inflation_beans', 'inflation_maize', 'inflation_potatoes', 'inflation_food_price_index']
    ]
    .mean()
    .reset_index()
)

# Calculate volatility (standard deviation) for each province
volatility = (
    province_inflation.groupby('provinces')[
        ['inflation_beans', 'inflation_maize', 'inflation_potatoes', 'inflation_food_price_index']
    ]
    .std()
    .round(2)
    .sort_values(by='inflation_food_price_index', ascending=False)  # Sort overall FPI volatility descending
)

print("\n📊 Inflation Volatility by Province (Standard Deviation):")
print(volatility)

# Identify and display most volatile provinces for each commodity
print("\n🔥 Most Volatile Provinces by Commodity (Descending Order):")
for col in volatility.columns:
    commodity = col.replace('inflation_', '').replace('_', ' ').title()
    sorted_volatility = volatility[col].sort_values(ascending=False)
    
    print(f"\n{commodity}:")
    print(sorted_volatility.to_string())
    print(f"➡️ Most volatile province: {sorted_volatility.index[0]} (std: {sorted_volatility.iloc[0]:.2f})")

📊 Inflation Volatility by Province (Standard Deviation):
               inflation_beans  inflation_maize  inflation_potatoes  \
provinces                                                             
Coast                    24.91            37.48               21.29   
Nyanza                   24.79            39.72               20.36   
Rift Valley              24.88            34.67               19.62   
Eastern                  24.77            34.62               19.57   
Central                  24.33            32.34               18.61   
North Eastern            24.83            31.75               18.21   
Nairobi                  25.11            30.33               17.05   

               inflation_food_price_index  
provinces                                  
Coast                               24.03  
Nyanza                              23.79  
Rift Valley                         23.67  
Eastern                             23.52  
Central                             22.87  
North Eastern                       22.87  
Nairobi                             22.08  

🔥 Most Volatile Provinces by Commodity (Descending Order):

Beans:
provinces
Nairobi          25.11
Coast            24.91
Rift Valley      24.88
North Eastern    24.83
Nyanza           24.79
Eastern          24.77
Central          24.33
➡️ Most volatile province: Nairobi (std: 25.11)

Maize:
provinces
Nyanza           39.72
Coast            37.48
Rift Valley      34.67
Eastern          34.62
Central          32.34
North Eastern    31.75
Nairobi          30.33
➡️ Most volatile province: Nyanza (std: 39.72)

Potatoes:
provinces
Coast            21.29
Nyanza           20.36
Rift Valley      19.62
Eastern          19.57
Central          18.61
North Eastern    18.21
Nairobi          17.05
➡️ Most volatile province: Coast (std: 21.29)

Food Price Index:
provinces
Coast            24.03
Nyanza           23.79
Rift Valley      23.67
Eastern          23.52
Central          22.87
North Eastern    22.87
Nairobi          22.08
➡️ Most volatile province: Coast (std: 24.03)

📊 Inflation Volatility by Province (Standard Deviation)¶

The table below shows how much inflation rates have fluctuated (standard deviation) across Kenyan provinces and commodities.

Province	Beans	Maize	Potatoes	Food Price Index
Coast	24.91	37.48	21.29	24.03
Nyanza	24.79	39.72	20.36	23.79
Rift Valley	24.88	34.67	19.62	23.67
Eastern	24.77	34.62	19.57	23.52
Central	24.33	32.34	18.61	22.87
North Eastern	24.83	31.75	18.21	22.87
Nairobi	25.11	30.33	17.05	22.08

🔥 Most Volatile Provinces by Commodity¶

Beans¶

Most volatile: Nairobi (std: 25.11)
Beans prices fluctuate quite evenly across all provinces (24–25 range).
Indicates widespread sensitivity to factors like climate and transportation costs.

Maize¶

Most volatile: Nyanza (std: 39.72)
Shows the highest overall volatility among all commodities.
Suggests maize prices are strongly affected by regional production shocks, rainfall patterns, and market demand.

Potatoes¶

Most volatile: Coast (std: 21.29)
Comparatively moderate volatility across regions.
Coastal variation may reflect supply-chain dependencies since potatoes are less grown locally.

Food Price Index (FPI)¶

Most volatile: Coast (std: 24.03)
Reflects general inflation movements; higher coastal volatility may indicate greater sensitivity to imported food prices and transport costs.

🧩 Insights¶

Maize is the most unstable commodity overall — major price swings across all provinces.
Beans show consistent volatility across the board, reflecting nationwide influences.
Potatoes are relatively stable, suggesting localized production and shorter supply chains.
The Coast and Nyanza provinces experience the highest overall food price fluctuations, while Nairobi shows slightly lower volatility likely due to better market access and price stabilization.

✅ Summary Table¶

Commodity	Most Volatile Province	Std. Dev	Observations
Beans	Nairobi	25.11	Even volatility across provinces
Maize	Nyanza	39.72	Highest volatility overall
Potatoes	Coast	21.29	Moderate, regional supply-driven
Food Price Index	Coast	24.03	Reflects import sensitivity and logistics costs

Inflation by County¶

# Group inflation data by province and year
county_inflation = (
    df.groupby(['provinces','counties','year'])[
        ['inflation_beans', 'inflation_maize', 'inflation_potatoes', 'inflation_food_price_index']
    ]
    .mean()
    .reset_index()
)

# Calculate volatility (standard deviation) for each province
volatility = (
    county_inflation.groupby('counties')[
        ['inflation_beans', 'inflation_maize', 'inflation_potatoes', 'inflation_food_price_index']
    ]
    .std()
    .round(2)
    .sort_values(by='inflation_food_price_index', ascending=False)  # Sort overall FPI volatility descending
)

print("\n📊 Inflation Volatility by County (Standard Deviation):")
print(volatility)

# Identify and display most volatile provinces for each commodity
print("\n🔥 Most Volatile County by Commodity (Descending Order):")
for col in volatility.columns:
    commodity = col.replace('inflation_', '').replace('_', ' ').title()
    sorted_volatility = volatility[col].sort_values(ascending=False)
    
    print(f"\n{commodity}:")
    print(sorted_volatility.to_string())
    print(f"➡️ Most volatile county: {sorted_volatility.index[0]} (std: {sorted_volatility.iloc[0]:.2f})")

📊 Inflation Volatility by County (Standard Deviation):
              inflation_beans  inflation_maize  inflation_potatoes  \
counties                                                             
Mombasa                 25.12            44.93               25.15   
Kilifi                  25.55            40.62               22.81   
Kwale                   24.62            42.49               23.86   
Samburu                 24.70            42.81               23.56   
Meru South              25.25            41.21               23.09   
Meru North              24.73            35.49               20.51   
West Pokot              25.13            34.89               20.09   
Nakuru                  24.80            41.65               22.18   
Marsabit                24.79            36.15               20.56   
Kisumu                  24.79            39.72               20.36   
Baringo                 24.77            35.43               20.25   
Taita Taveta            25.11            34.36               19.65   
Isiolo                  24.77            34.27               19.68   
Turkana                 25.16            32.75               18.87   
Kajiado                 26.17            31.20               17.98   
Makueni                 24.95            33.12               19.15   
Uasin Gishu             24.28            39.56               28.36   
Tana River              24.84            33.14               19.02   
Machakos                24.61            32.25               18.62   
Wajir                   24.69            32.05               18.37   
Garissa                 24.94            31.80               18.24   
Nyeri                   24.33            32.34               18.61   
Mandera                 24.77            31.45               18.05   
Nairobi                 25.11            30.33               17.05   
Kitui                   25.15            31.36               15.30   

              inflation_food_price_index  
counties                                  
Mombasa                            25.31  
Kilifi                             24.93  
Kwale                              24.83  
Samburu                            24.73  
Meru South                         24.71  
Meru North                         24.25  
West Pokot                         23.98  
Nakuru                             23.90  
Marsabit                           23.84  
Kisumu                             23.79  
Baringo                            23.77  
Taita Taveta                       23.70  
Isiolo                             23.56  
Turkana                            23.54  
Kajiado                            23.48  
Makueni                            23.44  
Uasin Gishu                        23.43  
Tana River                         23.25  
Machakos                           22.97  
Wajir                              22.92  
Garissa                            22.92  
Nyeri                              22.87  
Mandera                            22.73  
Nairobi                            22.08  
Kitui                              21.16  

🔥 Most Volatile County by Commodity (Descending Order):

Beans:
counties
Kajiado         26.17
Kilifi          25.55
Meru South      25.25
Turkana         25.16
Kitui           25.15
West Pokot      25.13
Mombasa         25.12
Taita Taveta    25.11
Nairobi         25.11
Makueni         24.95
Garissa         24.94
Tana River      24.84
Nakuru          24.80
Kisumu          24.79
Marsabit        24.79
Baringo         24.77
Mandera         24.77
Isiolo          24.77
Meru North      24.73
Samburu         24.70
Wajir           24.69
Kwale           24.62
Machakos        24.61
Nyeri           24.33
Uasin Gishu     24.28
➡️ Most volatile county: Kajiado (std: 26.17)

Maize:
counties
Mombasa         44.93
Samburu         42.81
Kwale           42.49
Nakuru          41.65
Meru South      41.21
Kilifi          40.62
Kisumu          39.72
Uasin Gishu     39.56
Marsabit        36.15
Meru North      35.49
Baringo         35.43
West Pokot      34.89
Taita Taveta    34.36
Isiolo          34.27
Tana River      33.14
Makueni         33.12
Turkana         32.75
Nyeri           32.34
Machakos        32.25
Wajir           32.05
Garissa         31.80
Mandera         31.45
Kitui           31.36
Kajiado         31.20
Nairobi         30.33
➡️ Most volatile county: Mombasa (std: 44.93)

Potatoes:
counties
Uasin Gishu     28.36
Mombasa         25.15
Kwale           23.86
Samburu         23.56
Meru South      23.09
Kilifi          22.81
Nakuru          22.18
Marsabit        20.56
Meru North      20.51
Kisumu          20.36
Baringo         20.25
West Pokot      20.09
Isiolo          19.68
Taita Taveta    19.65
Makueni         19.15
Tana River      19.02
Turkana         18.87
Machakos        18.62
Nyeri           18.61
Wajir           18.37
Garissa         18.24
Mandera         18.05
Kajiado         17.98
Nairobi         17.05
Kitui           15.30
➡️ Most volatile county: Uasin Gishu (std: 28.36)

Food Price Index:
counties
Mombasa         25.31
Kilifi          24.93
Kwale           24.83
Samburu         24.73
Meru South      24.71
Meru North      24.25
West Pokot      23.98
Nakuru          23.90
Marsabit        23.84
Kisumu          23.79
Baringo         23.77
Taita Taveta    23.70
Isiolo          23.56
Turkana         23.54
Kajiado         23.48
Makueni         23.44
Uasin Gishu     23.43
Tana River      23.25
Machakos        22.97
Wajir           22.92
Garissa         22.92
Nyeri           22.87
Mandera         22.73
Nairobi         22.08
Kitui           21.16
➡️ Most volatile county: Mombasa (std: 25.31)

📊 Inflation Volatility by County (Kenya, 2007–2025)¶

This analysis examines the volatility (standard deviation) of inflation rates across Kenyan counties for three staple commodities — beans, maize, and potatoes — as well as the overall food price index.
High standard deviation values indicate greater price instability (volatility).

🔹 Summary Table¶

County	Beans (σ)	Maize (σ)	Potatoes (σ)	Food Price Index (σ)
Mombasa	25.12	44.93	25.15	25.31
Kilifi	25.55	40.62	22.81	24.93
Kwale	24.62	42.49	23.86	24.83
Samburu	24.70	42.81	23.56	24.73
Meru South	25.25	41.21	23.09	24.71
Uasin Gishu	24.28	39.56	28.36	23.43
Kajiado	26.17	31.20	17.98	23.48
Nairobi	25.11	30.33	17.05	22.08
Kitui	25.15	31.36	15.30	21.16

(Full data available in appendix section.)

🔥 Most Volatile Counties by Commodity¶

Commodity	Most Volatile County	Std. Deviation (σ)	Remarks
🫘 Beans	Kajiado	26.17	Strong intra-year fluctuations possibly linked to inconsistent rainfall and market dependency.
🌽 Maize	Mombasa	44.93	Extremely volatile due to reliance on external maize inflows and transport disruptions.
🥔 Potatoes	Uasin Gishu	28.36	Reflects sensitivity to farming seasons and regional demand spikes.
🍱 Food Price Index	Mombasa	25.31	Highest composite volatility, suggesting systemic food market instability.

📈 Key Insights¶

Coastal counties (Mombasa, Kilifi, Kwale) exhibit the highest volatility across most commodities, likely due to supply chain dependencies and market integration with imports.
Uasin Gishu stands out for potato volatility, aligning with its role as a highland agricultural hub sensitive to weather and logistics.
Kajiado’s bean inflation volatility suggests vulnerability to localized production shocks and urban demand fluctuations.
Nairobi, despite being the capital, shows lower volatility in potatoes and the food price index — likely due to diversified supply sources and better price regulation.

🧮 Interpretation¶

High volatility (σ > 25) indicates unstable price environments, where consumers experience frequent and sharp inflation changes.
This instability can signal:

Poor market coordination between counties.
Disruptions in transport or logistics.
High seasonal dependency in agricultural production.
Exposure to external shocks (imports, droughts, policy shifts).

Conversely, lower volatility (σ < 20) reflects stable local food systems with smoother inflation trends.

🗺️ Regional Trends¶

Coastal Region: High volatility across all commodities (notably maize and food price index).
Highlands (Rift Valley): Stable for beans and maize, volatile for potatoes.
Arid/Northern Counties (Mandera, Garissa, Turkana): Moderate volatility — possibly due to less direct agricultural dependence and consistent food aid/import flows.

📚 Appendix: Full County Volatility Data¶

County	Beans (σ)	Maize (σ)	Potatoes (σ)	Food Price Index (σ)
Mombasa	25.12	44.93	25.15	25.31
Kilifi	25.55	40.62	22.81	24.93
Kwale	24.62	42.49	23.86	24.83
Samburu	24.70	42.81	23.56	24.73
Meru South	25.25	41.21	23.09	24.71
Meru North	24.73	35.49	20.51	24.25
West Pokot	25.13	34.89	20.09	23.98
Nakuru	24.80	41.65	22.18	23.90
Marsabit	24.79	36.15	20.56	23.84
Kisumu	24.79	39.72	20.36	23.79
Baringo	24.77	35.43	20.25	23.77
Taita Taveta	25.11	34.36	19.65	23.70
Isiolo	24.77	34.27	19.68	23.56
Turkana	25.16	32.75	18.87	23.54
Kajiado	26.17	31.20	17.98	23.48
Makueni	24.95	33.12	19.15	23.44
Uasin Gishu	24.28	39.56	28.36	23.43
Tana River	24.84	33.14	19.02	23.25
Machakos	24.61	32.25	18.62	22.97
Wajir	24.69	32.05	18.37	22.92
Garissa	24.94	31.80	18.24	22.92
Nyeri	24.33	32.34	18.61	22.87
Mandera	24.77	31.45	18.05	22.73
Nairobi	25.11	30.33	17.05	22.08
Kitui	25.15	31.36	15.30	21.16

Inflation By Market¶

# Group inflation data by province and year
county_inflation = (
    df.groupby(['provinces','counties','mkt_name','year'])[
        ['inflation_beans', 'inflation_maize', 'inflation_potatoes', 'inflation_food_price_index']
    ]
    .mean()
    .reset_index()
)

# Calculate volatility (standard deviation) for each province
volatility = (
    county_inflation.groupby('mkt_name')[
        ['inflation_beans', 'inflation_maize', 'inflation_potatoes', 'inflation_food_price_index']
    ]
    .std()
    .round(2)
    .sort_values(by='inflation_food_price_index', ascending=False)  # Sort overall FPI volatility descending
)

print("\n📊 Inflation Volatility by Market Name (Standard Deviation):")
print(volatility)

# Identify and display most volatile provinces for each commodity
print("\n🔥 Most Volatile Market by Commodity (Descending Order):")
for col in volatility.columns:
    commodity = col.replace('inflation_', '').replace('_', ' ').title()
    sorted_volatility = volatility[col].sort_values(ascending=False)
    
    print(f"\n{commodity}:")
    print(sorted_volatility.to_string())
    print(f"➡️ Most volatile Market: {sorted_volatility.index[0]} (std: {sorted_volatility.iloc[0]:.2f})")

📊 Inflation Volatility by Market Name (Standard Deviation):
                      inflation_beans  inflation_maize  inflation_potatoes  \
mkt_name                                                                     
Kalahari (Mombasa)              25.28            47.54               26.53   
Bangladesh (Mombasa)            24.91            47.41               26.48   
Moroto (Mombasa)                25.24            46.35               25.91   
Shonda (Mombasa)                25.31            46.63               25.85   
Porro                           25.26            46.06               24.96   
...                               ...              ...                 ...   
Kawangware (Nairobi)            25.36            29.82               16.49   
Dandora (Nairobi)               25.23            30.07               16.98   
Kibra (Nairobi)                 24.32            30.17               17.26   
Kitui                           24.31            31.29               19.71   
Nairobi                         25.13            30.57               22.33   

                      inflation_food_price_index  
mkt_name                                          
Kalahari (Mombasa)                         25.88  
Bangladesh (Mombasa)                       25.80  
Moroto (Mombasa)                           25.69  
Shonda (Mombasa)                           25.54  
Porro                                      25.49  
...                                          ...  
Kawangware (Nairobi)                       22.31  
Dandora (Nairobi)                          22.17  
Kibra (Nairobi)                            21.91  
Kitui                                      20.44  
Nairobi                                    20.40  

[184 rows x 4 columns]

🔥 Most Volatile Market by Commodity (Descending Order):

Beans:
mkt_name
Kakuma 4                           26.78
HongKong (Kakuma)                  26.32
Kitui town (Kitui)                 26.26
Kalobeyei (Village 1)              26.25
Illbissil Food Market (Kajiado)    26.24
Kajiado                            26.17
Wayu                               26.03
Dadaab town                        25.96
Lopur                              25.77
Hagadera (Daadab)                  25.77
Kakuma 3                           25.74
Kalobeyei (Village 2)              25.69
Naduat                             25.65
Aposta                             25.64
Lolupe                             25.64
Saretho                            25.62
Mukuru (Nairobi)                   25.60
Nanigi                             25.57
Ethiopia (Kakuma)                  25.55
Kalobeyei (Village 3)              25.55
Kilifi                             25.55
Kaeris                             25.51
Lokichoggio                        25.46
Alinjugur                          25.45
Kataboi                            25.41
Kangemi (Nairobi)                  25.37
Kawangware (Nairobi)               25.36
Eliye Centre                       25.36
Dagahaley (Daadab)                 25.34
Letea                              25.33
Damajale                           25.33
Kalokol                            25.32
Shonda (Mombasa)                   25.31
Kalahari (Mombasa)                 25.28
Kakuma 2                           25.27
Kongowea (Mombasa)                 25.26
Alemsekon                          25.26
Porro                              25.26
Kaanwa (Tharaka Nithi)             25.25
Shauri Yako                        25.24
Moroto (Mombasa)                   25.24
Dandora (Nairobi)                  25.23
Lorugum                            25.22
Namoruputh                         25.21
Jarajara                           25.21
Loturerei                          25.20
Lokitaung                          25.19
Lowarengak                         25.19
Kokuro                             25.18
Kibwezi                            25.17
Kaakelai                           25.17
Nakinomet                          25.16
Kalemunyang                        25.16
Kaikor                             25.15
Kaleng                             25.15
Garbatulla                         25.15
Napak                              25.15
Lomut (West Pokot)                 25.14
Makutano (West Pokot)              25.14
Nairobi                            25.13
El Wak                             25.13
Lokiriama                          25.12
Wakulima (Nairobi)                 25.12
Emali                              25.12
Mtito Andei                        25.11
Kisumu Ndogo (Mombasa)             25.11
Boka                               25.10
Tarbaj                             25.09
IFO (Daadab)                       25.09
Boji                               25.06
Kitengela (Kajiado)                25.05
Bangale                            25.05
Nakuru                             25.04
Garissa town (Garissa)             25.03
Garissa                            25.03
Katilia                            25.02
Mombasa                            25.02
Songa                              25.01
Benane                             25.01
Elelea                             25.01
Dub Goba                           25.01
Dirib                              25.01
Marsabit                           25.00
Marsabit town                      25.00
Karare                             24.99
Mathare (Nairobi)                  24.97
Lokangae                           24.97
Kimalel Center                     24.96
Merti CENTER                       24.95
Takaba (Mandera)                   24.94
Suguta                             24.94
Nakukulas                          24.94
Marigat town (Baringo)             24.93
Mogadishu (Kakuma)                 24.92
Chemolingot                        24.92
Bangladesh (Mombasa)               24.91
Hadado                             24.91
Malka GALLA                        24.89
Hola town                          24.89
Maralal                            24.89
Isiolo town                        24.88
Lolkunono                          24.88
Nginyang                           24.88
Loosuk                             24.88
Kalemgorok                         24.88
Bilbil                             24.87
Kiwanja                            24.87
Ngilai                             24.87
Ngurunit                           24.87
Bura                               24.86
Habaswein                          24.85
Kathonzweni (Makueni)              24.85
Kisumu                             24.85
Saka Town                          24.85
Iresaboru Centre                   24.84
Lororo Centre                      24.83
Makueni                            24.83
Kiliwaheri                         24.83
Wote town (Makueni)                24.82
Biliqo                             24.82
Charidende                         24.82
Modogashe                          24.82
Bulesa Bulesa                      24.81
Lesirkan                           24.81
Kargi South Horr                   24.81
Amaya                              24.80
Oldonyiro                          24.80
Koriema                            24.79
Junda (Mombasa)                    24.79
Korr                               24.78
Ashabito                           24.77
Laisamis Town                      24.77
Bute                               24.76
Griftu                             24.76
Lodwar town                        24.75
Kibuye (Kisumu)                    24.73
Morulem                            24.73
Kisima                             24.73
Lokori                             24.72
Lokwii                             24.72
Alango Arba                        24.72
Loboi Center                       24.71
Makere                             24.71
Banissa                            24.71
Rhamu                              24.70
Wamba                              24.69
Lokichar (Turkana)                 24.68
Malkadaka                          24.68
Lolkuniyani                        24.68
Loglogo                            24.68
Mandera town                       24.66
Maalamin                           24.66
Mandera                            24.66
Loyangalani                        24.65
Lokapel                            24.64
Merille Town                       24.63
Vanga (Kwale)                      24.62
Lotubae                            24.62
Tala Centre Market (Machakos)      24.61
Shanta Abak                        24.61
Marigat (Baringo)                  24.61
Liboi                              24.61
Ngaremara                          24.60
Sericho Center                     24.59
Lagbogol                           24.57
Eldas                              24.56
Wakulima (Nakuru)                  24.56
Baraki                             24.53
Kipini                             24.48
Tarasaa                            24.44
Lengusaka                          24.42
Lodungokwe                         24.42
Kargi                              24.42
Kipsing CENTER                     24.41
Dertu                              24.39
Garsen                             24.39
Lpus                               24.38
Naakalei                           24.37
Karatina (Nyeri)                   24.33
Kibra (Nairobi)                    24.32
Kitui                              24.31
Eldoret town (Uasin Gishu)         24.28
Baragoi                            24.27
Wajir town                         24.08
➡️ Most volatile Market: Kakuma 4 (std: 26.78)

Maize:
mkt_name
Kalahari (Mombasa)                 47.54
Bangladesh (Mombasa)               47.41
Shonda (Mombasa)                   46.63
Ngurunit                           46.55
Moroto (Mombasa)                   46.35
Maralal                            46.12
Porro                              46.06
Baragoi                            45.79
Lolkunono                          45.71
Kongowea (Mombasa)                 45.58
Loosuk                             45.20
Lesirkan                           44.83
Junda (Mombasa)                    44.79
Merille Town                       44.54
Shauri Yako                        43.97
Kisumu Ndogo (Mombasa)             43.85
Laisamis Town                      43.74
Ngilai                             43.29
Lolkuniyani                        43.19
Wakulima (Nakuru)                  43.11
Kibuye (Kisumu)                    42.67
Vanga (Kwale)                      42.49
Wamba                              42.38
Charidende                         41.70
Lengusaka                          41.33
Kaanwa (Tharaka Nithi)             41.21
Mombasa                            40.97
Nakuru                             40.62
Kilifi                             40.62
Eldoret town (Uasin Gishu)         39.56
Lpus                               38.93
Kisima                             38.90
Lodungokwe                         38.60
Isiolo town                        38.29
Kargi South Horr                   37.75
Biliqo                             37.63
Suguta                             37.50
Bulesa Bulesa                      37.35
Kisumu                             37.05
Chemolingot                        36.70
Korr                               36.61
Nginyang                           36.53
Malkadaka                          36.19
Merti CENTER                       36.08
Marigat (Baringo)                  36.08
Kiwanja                            35.80
Malka GALLA                        35.64
Boka                               35.53
Loglogo                            35.28
Marsabit                           35.27
Koriema                            35.27
Marigat town (Baringo)             35.24
Ngaremara                          35.19
Dub Goba                           35.19
Dirib                              35.18
Kimalel Center                     35.11
Karare                             35.10
Lororo Centre                      34.96
Makutano (West Pokot)              34.94
Lomut (West Pokot)                 34.92
Songa                              34.85
Marsabit town                      34.81
Iresaboru Centre                   34.80
Loboi Center                       34.76
Kakuma 3                           34.69
Amaya                              34.66
Mogadishu (Kakuma)                 34.44
Mtito Andei                        34.36
Kargi                              34.34
Dertu                              34.11
Kipsing CENTER                     34.06
Kibwezi                            33.99
Sericho Center                     33.96
Ethiopia (Kakuma)                  33.94
Oldonyiro                          33.90
Aposta                             33.90
Kakuma 4                           33.85
Hadado                             33.73
Alemsekon                          33.65
Lokichoggio                        33.58
Emali                              33.57
Kipini                             33.56
HongKong (Kakuma)                  33.54
Lopur                              33.53
Kakuma 2                           33.48
Lokori                             33.44
Lokangae                           33.43
Morulem                            33.41
Wayu                               33.40
Lotubae                            33.39
Lokwii                             33.32
Tarasaa                            33.25
Garissa town (Garissa)             33.23
Kalemgorok                         33.16
Elelea                             33.13
Damajale                           33.12
Bangale                            33.11
Garsen                             33.04
Katilia                            33.01
Nakukulas                          32.98
IFO (Daadab)                       32.97
Naduat                             32.95
Saka Town                          32.92
Nanigi                             32.90
Lokiriama                          32.89
Lolupe                             32.88
Kalobeyei (Village 3)              32.81
Garissa                            32.80
Napak                              32.76
Nakinomet                          32.75
Jarajara                           32.74
Makueni                            32.72
Kaikor                             32.71
Wote town (Makueni)                32.71
Kathonzweni (Makueni)              32.71
Namoruputh                         32.71
Eldas                              32.68
Lokapel                            32.66
Naakalei                           32.58
Lokichar (Turkana)                 32.58
Kaeris                             32.52
Loyangalani                        32.51
Kaakelai                           32.51
Kaleng                             32.51
Habaswein                          32.45
Lorugum                            32.43
Kokuro                             32.43
Hola town                          32.42
Maalamin                           32.36
Karatina (Nyeri)                   32.34
Benane                             32.34
Boji                               32.29
Lokitaung                          32.27
Tala Centre Market (Machakos)      32.25
Bute                               32.25
Kalemunyang                        32.24
Makere                             32.19
Kataboi                            32.19
Lowarengak                         32.19
Garbatulla                         32.18
Alango Arba                        32.18
Bura                               32.15
Letea                              32.13
Griftu                             32.08
Bilbil                             32.00
Tarbaj                             32.00
Kalokol                            31.98
Liboi                              31.94
Baraki                             31.87
Modogashe                          31.85
Lodwar town                        31.84
El Wak                             31.83
Loturerei                          31.81
Mathare (Nairobi)                  31.70
Eliye Centre                       31.61
Banissa                            31.60
Kiliwaheri                         31.59
Alinjugur                          31.56
Saretho                            31.51
Illbissil Food Market (Kajiado)    31.49
Dadaab town                        31.46
Ashabito                           31.45
Takaba (Mandera)                   31.43
Kitui town (Kitui)                 31.43
Mandera town                       31.31
Mandera                            31.31
Mukuru (Nairobi)                   31.30
Rhamu                              31.29
Shanta Abak                        31.29
Kitui                              31.29
Lagbogol                           31.28
Dagahaley (Daadab)                 31.13
Kalobeyei (Village 1)              31.00
Kajiado                            30.98
Kitengela (Kajiado)                30.96
Kalobeyei (Village 2)              30.82
Nairobi                            30.57
Wakulima (Nairobi)                 30.56
Wajir town                         30.53
Kibra (Nairobi)                    30.17
Kangemi (Nairobi)                  30.11
Dandora (Nairobi)                  30.07
Kawangware (Nairobi)               29.82
Hagadera (Daadab)                  29.45
➡️ Most volatile Market: Kalahari (Mombasa) (std: 47.54)

Potatoes:
mkt_name
Nakuru                             28.77
Eldoret town (Uasin Gishu)         28.36
Kalahari (Mombasa)                 26.53
Bangladesh (Mombasa)               26.48
Moroto (Mombasa)                   25.91
Shonda (Mombasa)                   25.85
Kongowea (Mombasa)                 25.35
Ngurunit                           25.31
Junda (Mombasa)                    25.26
Kisumu                             25.02
Baragoi                            24.97
Porro                              24.96
Lolkunono                          24.79
Maralal                            24.74
Shauri Yako                        24.69
Lesirkan                           24.59
Loosuk                             24.58
Kisumu Ndogo (Mombasa)             24.55
Merille Town                       24.54
Laisamis Town                      24.23
Ngilai                             23.93
Lolkuniyani                        23.88
Vanga (Kwale)                      23.86
Charidende                         23.57
Wamba                              23.46
Mombasa                            23.44
Wakulima (Nakuru)                  23.33
Kaanwa (Tharaka Nithi)             23.09
Lengusaka                          22.93
Kilifi                             22.81
Kibuye (Kisumu)                    22.42
Nairobi                            22.33
Isiolo town                        22.08
Lpus                               21.83
Kisima                             21.76
Lodungokwe                         21.66
Kargi South Horr                   21.49
Biliqo                             21.36
Bulesa Bulesa                      21.22
Suguta                             21.18
Chemolingot                        20.97
Korr                               20.86
Nginyang                           20.85
Kiwanja                            20.68
Malkadaka                          20.62
Merti CENTER                       20.57
Marigat (Baringo)                  20.51
Ngaremara                          20.34
Koriema                            20.32
Malka GALLA                        20.30
Boka                               20.29
Lomut (West Pokot)                 20.28
Kimalel Center                     20.22
Loglogo                            20.22
Marigat town (Baringo)             20.14
Marsabit                           20.12
Dirib                              20.08
Dub Goba                           20.08
Karare                             20.04
Makutano (West Pokot)              19.99
Lororo Centre                      19.99
Iresaboru Centre                   19.93
Kakuma 3                           19.91
Songa                              19.90
Loboi Center                       19.85
Marsabit town                      19.83
Mogadishu (Kakuma)                 19.73
Kitui                              19.71
Kargi                              19.69
Amaya                              19.66
Mtito Andei                        19.65
Dertu                              19.63
Kipsing CENTER                     19.62
Oldonyiro                          19.54
Kakuma 4                           19.54
Kibwezi                            19.52
Sericho Center                     19.52
Ethiopia (Kakuma)                  19.48
Aposta                             19.47
Alemsekon                          19.38
Kakuma 2                           19.36
Hadado                             19.33
Lopur                              19.30
Emali                              19.28
Lokichoggio                        19.26
Kipini                             19.24
Lokangae                           19.21
Lokori                             19.20
Wayu                               19.17
Lotubae                            19.16
Morulem                            19.16
HongKong (Kakuma)                  19.14
Lokwii                             19.12
Tarasaa                            19.10
Bangale                            19.09
Kalemgorok                         19.08
Elelea                             19.04
Garissa town (Garissa)             19.02
Lokiriama                          19.02
Kathonzweni (Makueni)              19.01
Makueni                            19.01
Wote town (Makueni)                19.00
Lolupe                             18.98
Katilia                            18.98
Garsen                             18.97
Nakukulas                          18.96
Saka Town                          18.94
Naduat                             18.94
Nanigi                             18.93
Napak                              18.90
Namoruputh                         18.90
Nakinomet                          18.90
Damajale                           18.89
Kalobeyei (Village 3)              18.88
Kaikor                             18.88
Garissa                            18.80
Lorugum                            18.79
IFO (Daadab)                       18.79
Kaakelai                           18.78
Kaeris                             18.78
Lokichar (Turkana)                 18.77
Kaleng                             18.77
Lokapel                            18.74
Jarajara                           18.74
Kokuro                             18.73
Eldas                              18.71
Naakalei                           18.69
Kalemunyang                        18.66
Loyangalani                        18.62
Tala Centre Market (Machakos)      18.62
Karatina (Nyeri)                   18.61
Lokitaung                          18.60
Hola town                          18.60
Boji                               18.60
Benane                             18.58
Kataboi                            18.57
Letea                              18.57
Mathare (Nairobi)                  18.55
Maalamin                           18.54
Lowarengak                         18.53
Alango Arba                        18.53
Habaswein                          18.52
Bute                               18.52
Kalokol                            18.48
Garbatulla                         18.48
Loturerei                          18.45
Lodwar town                        18.45
Griftu                             18.43
Makere                             18.38
Modogashe                          18.36
Bura                               18.35
Tarbaj                             18.34
Kitengela (Kajiado)                18.31
Baraki                             18.30
El Wak                             18.29
Bilbil                             18.29
Liboi                              18.26
Eliye Centre                       18.25
Illbissil Food Market (Kajiado)    18.20
Dadaab town                        18.13
Kiliwaheri                         18.13
Banissa                            18.11
Ashabito                           18.04
Shanta Abak                        18.03
Alinjugur                          18.01
Saretho                            18.01
Rhamu                              18.00
Mukuru (Nairobi)                   18.00
Mandera town                       17.99
Mandera                            17.99
Kalobeyei (Village 1)              17.97
Takaba (Mandera)                   17.97
Lagbogol                           17.91
Dagahaley (Daadab)                 17.90
Wakulima (Nairobi)                 17.90
Kalobeyei (Village 2)              17.85
Kajiado                            17.77
Wajir town                         17.53
Kibra (Nairobi)                    17.26
Dandora (Nairobi)                  16.98
Hagadera (Daadab)                  16.87
Kangemi (Nairobi)                  16.70
Kitui town (Kitui)                 16.67
Kawangware (Nairobi)               16.49
➡️ Most volatile Market: Nakuru (std: 28.77)

Food Price Index:
mkt_name
Kalahari (Mombasa)                 25.88
Bangladesh (Mombasa)               25.80
Moroto (Mombasa)                   25.69
Shonda (Mombasa)                   25.54
Porro                              25.49
Kongowea (Mombasa)                 25.45
Lolkunono                          25.24
Maralal                            25.24
Shauri Yako                        25.22
Wakulima (Nakuru)                  25.21
Loosuk                             25.18
Kisumu Ndogo (Mombasa)             25.16
Ngurunit                           25.08
Ngilai                             25.05
Junda (Mombasa)                    25.02
Kibuye (Kisumu)                    24.98
Lolkuniyani                        24.97
Lesirkan                           24.96
Merille Town                       24.94
Kilifi                             24.93
Isiolo town                        24.91
Laisamis Town                      24.85
Vanga (Kwale)                      24.83
Kakuma 4                           24.74
Wamba                              24.74
Mombasa                            24.73
Baragoi                            24.71
Kaanwa (Tharaka Nithi)             24.71
Kakuma 3                           24.56
HongKong (Kakuma)                  24.51
Charidende                         24.50
Lengusaka                          24.43
Kiwanja                            24.37
Lopur                              24.23
Kisima                             24.23
Ethiopia (Kakuma)                  24.18
Kargi South Horr                   24.17
Ngaremara                          24.13
Aposta                             24.08
Biliqo                             24.08
Makutano (West Pokot)              24.03
Lpus                               24.03
Alemsekon                          24.03
Chemolingot                        24.02
Bulesa Bulesa                      24.02
Suguta                             24.01
Wayu                               24.01
Lodungokwe                         23.99
Kimalel Center                     23.95
Lomut (West Pokot)                 23.94
Nginyang                           23.94
Marigat town (Baringo)             23.92
Korr                               23.90
Lokichoggio                        23.88
Marsabit                           23.88
Dirib                              23.87
Dub Goba                           23.87
Boka                               23.86
Merti CENTER                       23.85
Malkadaka                          23.85
Kalobeyei (Village 1)              23.84
Nanigi                             23.82
Naduat                             23.81
Kakuma 2                           23.80
Lolupe                             23.80
Songa                              23.78
Karare                             23.78
Malka GALLA                        23.76
Koriema                            23.76
Lororo Centre                      23.75
Marsabit town                      23.74
Kalobeyei (Village 3)              23.72
Kibwezi                            23.71
Mogadishu (Kakuma)                 23.70
Mtito Andei                        23.70
Kalobeyei (Village 2)              23.66
Oldonyiro                          23.66
Marigat (Baringo)                  23.64
Lokangae                           23.63
Amaya                              23.62
Loglogo                            23.61
Loboi Center                       23.61
Iresaboru Centre                   23.59
Kaeris                             23.59
Lokiriama                          23.57
Bangale                            23.55
Kipsing CENTER                     23.53
Namoruputh                         23.53
Kajiado                            23.52
Elelea                             23.52
Kalemgorok                         23.51
Nakukulas                          23.51
Katilia                            23.49
Lorugum                            23.49
Kataboi                            23.47
Illbissil Food Market (Kajiado)    23.46
Emali                              23.45
Eldoret town (Uasin Gishu)         23.43
Letea                              23.42
Hadado                             23.42
Lokori                             23.41
Kathonzweni (Makueni)              23.39
Morulem                            23.38
Kalokol                            23.38
Kalemunyang                        23.38
Napak                              23.37
Nakinomet                          23.37
Lokwii                             23.36
Jarajara                           23.36
Kaikor                             23.35
Makueni                            23.34
Lotubae                            23.33
Wote town (Makueni)                23.33
Kaakelai                           23.32
Kokuro                             23.31
Kaleng                             23.31
Eliye Centre                       23.30
Saka Town                          23.29
Sericho Center                     23.28
Dertu                              23.28
Garissa town (Garissa)             23.28
Lokitaung                          23.27
Kargi                              23.27
Garbatulla                         23.27
Benane                             23.27
Dadaab town                        23.27
Nakuru                             23.26
Damajale                           23.26
Boji                               23.25
Kipini                             23.25
Lowarengak                         23.25
Loturerei                          23.24
Lokichar (Turkana)                 23.17
Lokapel                            23.15
Garissa                            23.15
Kisumu                             23.13
El Wak                             23.13
Tarasaa                            23.13
Tarbaj                             23.12
Alango Arba                        23.11
Loyangalani                        23.11
Saretho                            23.09
Lodwar town                        23.09
Hola town                          23.08
IFO (Daadab)                       23.05
Maalamin                           23.03
Garsen                             23.02
Naakalei                           23.01
Griftu                             22.99
Eldas                              22.99
Tala Centre Market (Machakos)      22.97
Alinjugur                          22.96
Bute                               22.95
Bura                               22.93
Habaswein                          22.91
Mukuru (Nairobi)                   22.91
Bilbil                             22.89
Karatina (Nyeri)                   22.87
Modogashe                          22.84
Makere                             22.82
Dagahaley (Daadab)                 22.82
Liboi                              22.78
Mandera                            22.75
Mandera town                       22.75
Takaba (Mandera)                   22.70
Rhamu                              22.68
Lagbogol                           22.68
Kiliwaheri                         22.68
Ashabito                           22.66
Kitui town (Kitui)                 22.64
Shanta Abak                        22.63
Banissa                            22.61
Hagadera (Daadab)                  22.58
Mathare (Nairobi)                  22.57
Baraki                             22.57
Kitengela (Kajiado)                22.46
Wajir town                         22.41
Kangemi (Nairobi)                  22.40
Wakulima (Nairobi)                 22.36
Kawangware (Nairobi)               22.31
Dandora (Nairobi)                  22.17
Kibra (Nairobi)                    21.91
Kitui                              20.44
Nairobi                            20.40
➡️ Most volatile Market: Kalahari (Mombasa) (std: 25.88)

Inflation Volatility Breakdown (Kenyan Markets)¶

🫘 Beans¶

Top 5 Most Volatile Markets

Kakuma 4 — 26.78
HongKong (Kakuma) — 26.32
Kitui town (Kitui) — 26.26
Kalobeyei (Village 1) — 26.25
Illbissil Food Market (Kajiado) — 26.24

🌀 Most Volatile Overall: Kakuma 4
Observation:

High volatility across Kakuma region markets, likely due to refugee camp–driven supply instability.
Nairobi markets (e.g., Mukuru, Kangemi, Kawangware) show moderate volatility (~25.3–25.6).

🌽 Maize¶

Top 5 Most Volatile Markets

Kalahari (Mombasa) — 47.54
Bangladesh (Mombasa) — 47.41
Shonda (Mombasa) — 46.63
Ngurunit — 46.55
Moroto (Mombasa) — 46.35

🌀 Most Volatile Overall: Kalahari (Mombasa)
Observation:

Coastal markets (Mombasa region) dominate volatility in maize prices.
Reflects import dependency and logistical sensitivity at coastal entry points.

🥔 Potatoes¶

Top 5 Most Volatile Markets

Nakuru — 28.77
Eldoret town (Uasin Gishu) — 28.36
Kalahari (Mombasa) — 26.53
Bangladesh (Mombasa) — 26.48
Moroto (Mombasa) — 25.91

🌀 Most Volatile Overall: Nakuru
Observation:

Rift Valley (Nakuru, Eldoret) shows highest volatility, likely from seasonal production swings.
Mombasa remains volatile despite being a consumer/import market — indicating transport cost pass-throughs.

🍚 Food Price Index (General Inflation)¶

Top 5 Most Volatile Markets

Kalahari (Mombasa) — 25.88
Bangladesh (Mombasa) — 25.80
Moroto (Mombasa) — 25.69
Shonda (Mombasa) — 25.54
Porro — 25.49

🌀 Most Volatile Overall: Kalahari (Mombasa)
Observation:

Mombasa consistently shows highest volatility across commodities.
Nairobi and Kitui markets have relatively stable inflation patterns (FPI std ~20–22).

📈 Regional Trends Summary¶

Region	Typical Volatility Drivers	Notes
Mombasa (Coast)	Import dependency, port logistics	Highest volatility across all commodities
Kakuma (Turkana)	Remote location, aid-market effects	High volatility in beans
Rift Valley (Nakuru, Eldoret)	Seasonal production	Highest for potatoes
Nairobi	Urban demand stability	Lowest volatility overall

🔎 Insights¶

Mombasa is Kenya’s most volatile food market overall.
Rift Valley dominates in production-related fluctuations.
Refugee-dense and arid regions (Turkana, Garissa) show frequent shocks in basic staples.
Urban centers (Nairobi, Kitui) are price-stable, likely due to diversified supply chains.

	latitude	longitude	year	month	o_beans	h_beans	l_beans	c_beans	inflation_beans	trust_beans	...	l_potatoes	c_potatoes	inflation_potatoes	trust_potatoes	o_food_price_index	h_food_price_index	l_food_price_index	c_food_price_index	inflation_food_price_index	trust_food_price_index
count	41400.000000	41400.000000	41400.00000	41400.000000	41400.000000	41400.000000	41400.000000	41400.000000	39192.000000	41400.000000	...	41400.000000	41400.000000	39192.000000	41400.000000	41400.000000	41400.000000	41400.000000	41400.000000	39192.000000	41400.000000
mean	0.938696	37.579239	2015.88000	6.440000	98.168466	102.572487	93.781520	98.177126	9.031954	9.145188	...	1981.099405	2060.098150	8.249555	5.495944	0.987793	1.022464	0.953249	0.988657	9.395280	9.142923
std	2.219824	1.897270	5.41353	3.434145	27.899955	28.637733	26.989826	27.611295	28.478154	0.185153	...	529.491075	547.465454	25.442221	0.449740	0.291554	0.298071	0.283049	0.288419	27.203402	0.177494
min	-4.660000	34.360000	2007.00000	1.000000	21.690000	24.480000	20.640000	22.050000	-50.370000	9.100000	...	319.050000	380.120000	-70.540000	5.400000	0.320000	0.350000	0.310000	0.330000	-45.150000	9.100000
25%	-0.325000	35.967500	2011.00000	3.000000	81.240000	85.190000	77.230000	81.280000	-8.300000	9.100000	...	1650.070000	1715.710000	-9.172500	5.400000	0.810000	0.840000	0.780000	0.810000	-8.360000	9.100000
50%	0.970000	37.320000	2016.00000	6.000000	97.310000	101.630000	92.870000	97.230000	1.590000	9.100000	...	1941.540000	2013.820000	3.730000	5.400000	0.960000	1.000000	0.930000	0.960000	2.480000	9.100000
75%	2.502500	39.572500	2021.00000	9.000000	113.000000	117.600000	108.130000	112.890000	22.340000	9.100000	...	2243.167500	2334.785000	22.300000	5.400000	1.140000	1.180000	1.100000	1.140000	24.540000	9.100000
max	4.620000	41.860000	2025.00000	12.000000	215.440000	225.520000	200.290000	213.290000	135.880000	10.000000	...	5105.320000	5671.570000	404.810000	10.000000	2.260000	2.350000	2.080000	2.130000	113.280000	10.000000

	year	beans_close	maize_close	potatoes_1kg_close	food_price_index_close
0	2007	45.640113	13.079375	22.634065	0.461155
1	2008	79.911608	20.551902	29.716902	0.738066
2	2009	81.705702	27.219180	35.692530	0.819977
3	2010	63.598777	18.374216	27.903323	0.622885
4	2011	96.815100	32.067518	39.292747	0.953514
5	2012	98.124266	34.243551	41.427033	0.985353
6	2013	91.081843	31.461594	39.123011	0.916581
7	2014	94.363143	31.994601	39.510317	0.940403
8	2015	91.108315	29.816128	37.854715	0.900005
9	2016	86.025534	30.630245	39.013446	0.882500
10	2017	109.346952	41.139620	46.192669	1.115077
11	2018	90.596766	26.784538	35.813045	0.868587
12	2019	98.113379	32.881585	40.541108	0.972545
13	2020	103.517822	33.434588	40.744212	1.007391
14	2021	108.550648	44.024253	48.192428	1.138302
15	2022	117.875593	41.151698	46.073577	1.162708
16	2023	151.615557	67.641803	63.258623	1.601599
17	2024	139.988546	54.086662	55.008107	1.412133
18	2025	123.788587	61.091818	59.393267	1.384728

	year	month	beans_close	maize_close	potatoes_1kg_close	food_price_index_close
0	2007	1	46.146848	13.203098	22.837568	0.466196
1	2007	2	44.950109	13.179891	22.827742	0.458750
2	2007	3	42.479076	12.173207	21.831339	0.433152
3	2007	4	44.402011	12.440870	22.138103	0.448098
4	2007	5	42.239457	12.872065	22.408171	0.439457
...	...	...	...	...	...	...
220	2025	5	131.143587	63.099022	60.603499	1.444348
221	2025	6	131.127446	63.093913	60.597341	1.444239
222	2025	7	125.509891	60.347772	58.975321	1.388043
223	2025	8	108.025870	65.642065	62.114737	1.336630
224	2025	9	107.132174	63.510435	60.868936	1.312663

	counties	year	beans_close	maize_close	potatoes_1kg_close	food_price_index_close
0	Baringo	2007	39.981759	9.070648	18.166039	0.381111
1	Baringo	2008	69.886667	14.259815	24.055556	0.613148
2	Baringo	2009	71.460093	18.932685	28.765706	0.676019
3	Baringo	2010	55.676574	12.716296	22.336981	0.513889
4	Baringo	2011	83.811296	22.299167	31.651383	0.780648
...	...	...	...	...	...	...
470	West Pokot	2021	106.917500	30.592917	38.749200	0.999583
471	West Pokot	2022	107.625000	38.933750	45.029183	1.085417
472	West Pokot	2023	138.033333	68.738750	64.208533	1.536667
473	West Pokot	2024	127.336667	53.448750	54.868575	1.335833
474	West Pokot	2025	111.733889	62.616111	60.615422	1.332222

	counties	year	beans_close	maize_close	potatoes_1kg_close	food_price_index_close	provinces	county_label	avg_food_price
0	Baringo	2007	39.981759	9.070648	18.166039	0.381111	Rift Valley	Baringo (Rift Valley)	22.406149
1	Baringo	2008	69.886667	14.259815	24.055556	0.613148	Rift Valley	Baringo (Rift Valley)	36.067346
2	Baringo	2009	71.460093	18.932685	28.765706	0.676019	Rift Valley	Baringo (Rift Valley)	39.719494
3	Baringo	2010	55.676574	12.716296	22.336981	0.513889	Rift Valley	Baringo (Rift Valley)	30.243284
4	Baringo	2011	83.811296	22.299167	31.651383	0.780648	Rift Valley	Baringo (Rift Valley)	45.920615
...	...	...	...	...	...	...	...	...	...
470	West Pokot	2021	106.917500	30.592917	38.749200	0.999583	Rift Valley	West Pokot (Rift Valley)	58.753206
471	West Pokot	2022	107.625000	38.933750	45.029183	1.085417	Rift Valley	West Pokot (Rift Valley)	63.862644
472	West Pokot	2023	138.033333	68.738750	64.208533	1.536667	Rift Valley	West Pokot (Rift Valley)	90.326872
473	West Pokot	2024	127.336667	53.448750	54.868575	1.335833	Rift Valley	West Pokot (Rift Valley)	78.551331
474	West Pokot	2025	111.733889	62.616111	60.615422	1.332222	Rift Valley	West Pokot (Rift Valley)	78.321807

	provinces	counties	mkt_name	latitude	longitude	geo_id	price_date	year	month	components	...	l_potatoes	c_potatoes	inflation_potatoes	trust_potatoes	o_food_price_index	h_food_price_index	l_food_price_index	c_food_price_index	inflation_food_price_index	trust_food_price_index
0	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	1/1/2007	2007	1	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1172.65	1214.63	NaN	5.4	0.49	0.50	0.47	0.49	NaN	9.1
1	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	2/1/2007	2007	2	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1175.46	1197.46	NaN	5.4	0.48	0.50	0.47	0.48	NaN	9.1
2	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	3/1/2007	2007	3	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1147.22	1147.22	NaN	5.4	0.47	0.48	0.45	0.45	NaN	9.1
3	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	4/1/2007	2007	4	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1095.18	1159.48	NaN	5.4	0.44	0.46	0.43	0.46	NaN	9.1
4	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	5/1/2007	2007	5	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1138.89	1176.51	NaN	5.4	0.47	0.48	0.46	0.46	NaN	9.1
5	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	6/1/2007	2007	6	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1154.62	1184.45	NaN	5.4	0.45	0.47	0.44	0.47	NaN	9.1
6	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	7/1/2007	2007	7	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1160.40	1187.24	NaN	5.4	0.47	0.48	0.46	0.47	NaN	9.1
7	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	8/1/2007	2007	8	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1158.50	1193.78	NaN	5.4	0.47	0.48	0.45	0.47	NaN	9.1
8	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	9/1/2007	2007	9	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1168.62	1195.18	NaN	5.4	0.47	0.48	0.46	0.47	NaN	9.1
9	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	10/1/2007	2007	10	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1165.66	1188.42	NaN	5.4	0.47	0.48	0.46	0.47	NaN	9.1
10	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	11/1/2007	2007	11	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1156.61	1248.76	NaN	5.4	0.47	0.51	0.45	0.51	NaN	9.1
11	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	12/1/2007	2007	12	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1208.29	1208.29	NaN	5.4	0.54	0.58	0.52	0.58	NaN	9.1
12	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	1/1/2008	2008	1	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1157.56	1287.63	6.01	5.4	0.62	0.63	0.60	0.63	29.25	9.1
13	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	2/1/2008	2008	2	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1301.20	1304.50	8.94	5.4	0.66	0.68	0.64	0.67	40.28	9.1
14	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	3/1/2008	2008	3	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1276.05	1348.77	17.57	5.4	0.69	0.71	0.67	0.67	47.49	9.1
15	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	4/1/2008	2008	4	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1342.67	1442.09	24.37	5.4	0.67	0.69	0.65	0.67	43.14	9.1
16	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	5/1/2008	2008	5	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1451.52	1599.98	35.99	5.4	0.67	0.71	0.65	0.71	56.56	9.1
17	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	6/1/2008	2008	6	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1649.88	1689.73	42.66	5.4	0.74	0.76	0.72	0.73	56.03	9.1
18	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	7/1/2008	2008	7	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1688.77	1706.34	43.72	5.4	0.74	0.79	0.72	0.79	69.91	9.1
19	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	8/1/2008	2008	8	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1676.26	1678.12	40.57	5.4	0.82	0.85	0.80	0.83	77.14	9.1
20	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	9/1/2008	2008	9	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1620.27	1723.69	44.22	5.4	0.85	0.88	0.83	0.84	79.54	9.1
21	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	10/1/2008	2008	10	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1708.71	1759.42	48.05	5.4	0.85	0.92	0.83	0.92	97.57	9.1
22	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	11/1/2008	2008	11	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1730.03	1818.37	45.61	5.4	0.96	0.99	0.94	0.94	81.81	9.1
23	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	12/1/2008	2008	12	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1807.36	1847.46	52.90	5.4	0.95	0.98	0.84	0.84	44.29	9.1
24	North Eastern	Garissa	Alango Arba	-0.1	39.99	gid_-1000000399900000	1/1/2009	2009	1	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1812.60	1817.62	41.16	5.4	0.80	0.83	0.77	0.83	31.46	9.1

	provinces	counties	mkt_name	latitude	longitude	geo_id	price_date	year	month	components	...	food_price_index_open	food_price_index_high	food_price_index_low	food_price_index_close	inflation_food_price_index	trust_food_price_index	potatoes_1kg_open	potatoes_1kg_high	potatoes_1kg_low	potatoes_1kg_close
0	Central	Nyeri	Karatina (Nyeri)	-0.48	37.13	gid_-4800000371300000	1/1/2007	2007	1	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	0.47	0.48	0.45	0.47	NaN	9.1	24.4760	24.8922	23.5994	24.4304
1	Central	Nyeri	Karatina (Nyeri)	-0.48	37.13	gid_-4800000371300000	2/1/2007	2007	2	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	0.47	0.48	0.45	0.46	NaN	9.1	24.2006	24.8458	23.5554	23.9514
2	Central	Nyeri	Karatina (Nyeri)	-0.48	37.13	gid_-4800000371300000	3/1/2007	2007	3	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	0.45	0.47	0.43	0.43	NaN	9.1	23.6996	24.3288	23.0176	23.0176
3	Central	Nyeri	Karatina (Nyeri)	-0.48	37.13	gid_-4800000371300000	4/1/2007	2007	4	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	0.42	0.44	0.41	0.44	NaN	9.1	22.5254	23.1288	21.9222	23.0490
4	Central	Nyeri	Karatina (Nyeri)	-0.48	37.13	gid_-4800000371300000	5/1/2007	2007	5	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	0.45	0.46	0.43	0.44	NaN	9.1	23.1008	23.7028	22.4988	23.6062
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
41395	Rift Valley	Samburu	Wamba	0.98	37.32	gid_9800000373200000	5/1/2025	2025	5	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1.31	1.35	1.28	1.35	4.22	9.1	58.1402	60.2166	56.0638	59.9014
41396	Rift Valley	Samburu	Wamba	0.98	37.32	gid_9800000373200000	6/1/2025	2025	6	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1.37	1.41	1.34	1.35	5.67	9.1	60.7188	62.8504	58.5870	59.9462
41397	Rift Valley	Samburu	Wamba	0.98	37.32	gid_9800000373200000	7/1/2025	2025	7	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1.36	1.39	1.28	1.28	5.13	9.1	59.9174	62.0312	57.5610	57.5610
41398	Rift Valley	Samburu	Wamba	0.98	37.32	gid_9800000373200000	8/1/2025	2025	8	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1.25	1.29	1.22	1.23	-2.63	9.1	56.3176	60.1112	54.2932	60.1112
41399	Rift Valley	Samburu	Wamba	0.98	37.32	gid_9800000373200000	9/1/2025	2025	9	beans (1 KG, Index Weight = 1), maize (1 KG, I...	...	1.20	1.23	1.16	1.20	-0.31	9.1	60.9484	63.0852	58.8118	58.8740

	provinces	year	beans_close	maize_close	potatoes_1kg_close	food_price_index_close
0	Central	2007	43.306667	14.065833	23.800267	0.459167
1	Central	2008	74.847500	22.003333	30.878350	0.724167
2	Central	2009	77.480833	29.358333	37.670917	0.819167
3	Central	2010	60.275833	19.687500	29.007200	0.617500
4	Central	2011	90.697500	34.045000	40.623267	0.937500
...	...	...	...	...	...	...
128	Rift Valley	2021	109.717056	39.323788	45.220418	1.101429
129	Rift Valley	2022	116.934697	41.536439	46.611907	1.162565
130	Rift Valley	2023	151.666353	70.990249	65.547698	1.633874
131	Rift Valley	2024	140.577229	53.127662	54.740280	1.408344
132	Rift Valley	2025	124.888889	62.162035	60.351189	1.402525

	mkt_name	year	beans_close	maize_close	potatoes_1kg_close	food_price_index_close
0	Alango Arba	2007	46.450833	14.410000	23.835700	0.481667
1	Alango Arba	2008	80.490833	23.034167	32.010167	0.770000
2	Alango Arba	2009	82.659167	30.299167	38.218767	0.857500
3	Alango Arba	2010	64.414167	20.539167	29.866667	0.650000
4	Alango Arba	2011	97.013333	35.906667	42.129167	0.991667
...	...	...	...	...	...	...
3491	Wote town (Makueni)	2021	100.047500	43.669167	48.013917	1.087500
3492	Wote town (Makueni)	2022	102.910000	39.663333	45.069467	1.063333
3493	Wote town (Makueni)	2023	131.051667	62.497500	59.910550	1.438333
3494	Wote town (Makueni)	2024	120.897500	49.476667	51.972333	1.261667
3495	Wote town (Makueni)	2025	105.516667	55.046667	55.548244	1.225556

	year	inflation_beans	inflation_maize	inflation_potatoes	inflation_food_price_index
0	2007	NaN	NaN	NaN	NaN
1	2008	76.315765	56.879592	31.417835	60.150919
2	2009	4.893913	38.042763	22.311381	13.429416
3	2010	-20.739026	-32.248533	-21.447378	-23.429946
4	2011	55.745082	88.456653	45.533238	56.202926
5	2012	2.391413	13.948148	8.158818	5.209343
6	2013	-7.148972	-7.718302	-5.306952	-6.883433
7	2014	3.977138	2.088442	1.213080	2.859937
8	2015	-3.212351	-5.952237	-3.756499	-3.962360
9	2016	-5.412269	3.614774	3.648777	-1.687437
10	2017	27.401214	35.553564	19.406440	26.811993
11	2018	-16.570856	-34.257749	-21.988492	-21.763514
12	2019	9.599112	28.776504	15.532052	13.917396
13	2020	6.045516	5.008895	2.137391	4.585688
14	2021	6.002355	32.994040	18.794257	13.657659
15	2022	8.968587	-3.970670	-3.384239	2.491997
16	2023	28.296830	74.229307	40.466001	38.552120
17	2024	-5.188342	-19.182532	-12.665095	-10.718066
18	2025	-14.730568	14.373213	8.478653	-3.544559